Plant water uptake

Plant water uptake DEFAULT

How Does Silicon Mediate Plant Water Uptake and Loss Under Water Deficiency?

Introduction

Silicon (Si) is the second most abundant element in soil. Plants generally take up Si in the form of soluble monosilicic acid H4SiO4, which normally ranges from 0.1 to 0.6 mM in the soil solution (Ma and Yamaji, 2006). All terrestrial plants contain Si in their tissues although the contents of Si varies considerably among species, ranging from 0.1 to 10% Si on a dry weight basis (Ma and Yamaji, 2006; Cornelis et al., 2010; Sahebi et al., 2015). Si has not been recognized as an essential element for plant growth, it does exert beneficial effects for many plant species, including both monocots and dicots (Ma and Yamaji, 2015). Indeed, Si seems to alleviate the detrimental effects of various stresses, including drought, salinity, heat, cold, metal toxicity, nutrient imbalance, plant pathogens, and insect pests (Liang et al., 2007; Guntzer et al., 2012; Hernandez-Apaolaza, 2014; Zhang et al., 2014; Meharg and Meharg, 2015; Vivancos et al., 2015; Guo et al., 2016; Reynolds et al., 2016).

Water deficiency is one of the major environmental constraints of plant growth and crop productivity (Chaves and Oliveira, 2004; Verslues et al., 2006). Plant water deficiency may result from a shortage of water in soil (drought) or from an obstacle to water uptake (physiological drought). Plant water deficiency can also be caused by the excessive high vapor pressure deficit in the atmosphere, which results in higher rates of water loss via transpiration than the rates of water transport to the leaves (Mahajan and Tuteja, 2005). In these cases, plant water status is disturbed, resulting in disruption of important metabolic processes and reduction in growth rates (Verslues et al., 2006). Hence, investigating mechanisms of plants’ ability to tolerate water stress may lead to an understanding of how to increase water stress resistance. Recently, improvement of plant resistance to drought, osmotic, and salt stresses have been widely observed after the addition of Si to the growth medium (Zhu and Gong, 2014; Rizwan et al., 2015; Coskun et al., 2016; Helaly et al., 2017).

Several different aspects are involved in Si-improved plants’ resistance to drought or salt stress, including maintenance of nutrient balance, promotion of photosynthetic rate, increasing antioxidant capacity, and sequestration of toxic ions (Ma, 2004; Liang et al., 2007; Sacała, 2009; Zhu and Gong, 2014; Rizwan et al., 2015). Moreover, various compounds of Si, including 1–2 mM Na2SiO3, K2SiO3 or H2SiO3, either applied in the soil or the nutrient solution, are showed to improve the water status of plants experiencing drought or salt stress (Romero-Aranda et al., 2006; Sacała, 2009; Liu et al., 2014, 2015). In addition, it has been reported that supplement with 1 mM H2SiO3 in the nutrient solution can alleviate K deficiency, which also causes tissue dehydration (Chen et al., 2016). A variety of beneficial effects of Si application could be ascribed to the alleviation of problematic water status in those studies by decreasing the transpiration rate, increasing the osmotic adjustment capacity, or increasing water uptake (Liang et al., 2007; Sacała, 2009; Zhu and Gong, 2014; Rizwan et al., 2015). In this review, we address recent results that are relevant to the Si effect, and assess what they mean for the interpretation of how Si improves plant water status and enables the maintenance of plant water balance under water deficiency condition.

Silicon Contributes to Alleviation of Plant Water Status Under Stress Conditions

A common consequence of several abiotic stresses is the disturbance of plant water status. Abiotic stresses, such as drought, salinity, and freezing have a common impact on plant cells in decreasing the availability of water (Mahajan and Tuteja, 2005; Verslues et al., 2006), quantified as a decrease in plant water potential and relative water content. Conversely, maintenance of higher relative water contents indicates a better water status (Verslues et al., 2006).

Under drought stress, the beneficial effect of Si on plant water status has been extensively examined in various plant species, including sorghum (Hattori et al., 2007; Yin et al., 2013; Ahmed et al., 2014), wheat (Gong and Chen, 2012), maize (Amin et al., 2014), rice (Ming et al., 2012), cucumber (Ma et al., 2004), Kentucky Bluegrass (Saud et al., 2014), canola (Habibi, 2014), sunflower (Gunes et al., 2008), chickpea (Gunes et al., 2007), soybean (Shen et al., 2010), alfalfa (Liu and Guo, 2013), and tomato (Shi et al., 2016). The improvements of relative water content and/or water potential by Si application occurred under both polyethylene glycol-induced osmotic stress (Hattori et al., 2007; Ming et al., 2012) and potted soil drought conditions (Gong et al., 2003; Amin et al., 2014). In addition, it has been showed that in the leaves of Si-treated wheat, both relative water contents and the water potential were maintained to a greater extent compared to that without Si-treatment, suggesting that Si could also be used to improve the water status of wheat under field drought conditions (Gong and Chen, 2012).

Under salt stress condition, the beneficial role of Si in mitigating the adverse effects of salinity by preventing root Na+ uptake and/or its transport from roots to shoots has been widely reported (Liang et al., 2007; Savvas et al., 2007, 2009; Zhu and Gong, 2014; Savvas and Ntatsi, 2015). In addition to ion toxicity, high concentrations of salts in solution also cause osmotic stress in plants, because they limit the availability of water, affecting water status and leaf growth (Munns and Tester, 2008). Chen et al. (2014) reported that Si could alleviate the salt stress in both two phases of growth inhibition, with the alleviative effects being more pronounced in the osmotic stress phase than ion toxicity phase. Moreover, Si application is widely reported to improve the leaf relative water contents and/or leaf water potential under salt stress in wheat (Tuna et al., 2008), rice (Yeo et al., 1999), sorghum (Liu et al., 2015), maize (Parveen and Ashraf, 2010), tomato (Li et al., 2015), Phaseolus vulgaris (Zuccarini, 2008), sunflower (Ashraf et al., 2015), and cucumber (Wang et al., 2015). The only exception to these findings was the observation that Si decreased tomato leaf water potential under salt stress (Romero-Aranda et al., 2006). However, in this study, plant water content in salinized plants supplied with Si was 40% higher than in salinized plants without Si, and leaf turgor potential and net photosynthetic rates were much higher in salinized plants with Si. Therefore, in spite of the leaf water potential, it can be concluded that Si improves the water status of tomato under salt stress.

A recent study in sorghum showed that Si could alleviate potassium (K) deficiency by improving plant water status (Chen et al., 2016). K is the most abundant cation in plants and plays a key role in osmotic processes that contribute to cellular turgor, photosynthesis, and transpiration (Wang and Wu, 2013). K is involved in regulating the plant water status, and severe K deficiency causes tissue dehydration (Kanai et al., 2011). Moreover, it has also been reported that Si could enhance freezing stress resistance in freezing-susceptible wheat cultivar by alleviating water-deficit stress that caused by freezing-induced cellular dehydration (Liang et al., 2008).

Silicon Contributes to Maintaining Higher Transpiration Under Stress Conditions

Under normal growth conditions, water is absorbed by the roots and lost from the leaves, and plants keep a proper water balance by continuously adjusting these two processes (Maurel and Chrispeels, 2001). Under water deficient conditions, the plant’s first response is to avoid low water potential by adjust their water balance between root water uptake and leaf water loss (Luu and Maurel, 2005; Verslues et al., 2006). Plants can reduce leaf water loss by controlling the transpiration rate and also by decreasing their leaf area. Under normal growth conditions, only a few reports have shown that Si affects the transpiration rate. The pioneering researchers in this field reported that Si application can reduce the excessive leaf transpiration in rice and sugarcane under normal growth conditions; they postulated that this effect could be due to the reduction in transpiration rate through cuticular layers thickened by silica deposits (Yoshida, 1965; Savant et al., 1996). However, other researchers reported that rather than due to the thickness of cuticular layers, the reduced transpiration levels of Si-fed maize and rice were primarily due to the lower transpiration through stomatal pores (Agarie et al., 1998; Gao et al., 2004, 2006), which mainly ascribed to the turgor loss of guard cells originating from Si deposition and changing of the physical and mechanical properties of their cell walls (Ueno and Agarie, 2005; Savvas and Ntatsi, 2015). The reduced transpiration rates caused by Si were also reported in upland rice and cucumber in the absence of stress (Ma et al., 2004; Ming et al., 2012). Despite these reports, Si application has been found to have no effect on transpiration rates under normal growth conditions in the vast majority of studies (Hattori et al., 2005, 2009; Chen et al., 2011; Gong and Chen, 2012). We suspect that the conflicting results are due to species and genotypic variations since we have noticed that the effects of Si on reducing the transpiration rate under normal growth conditions tend to appear in the species and genotypes with high Si accumulation and high contribution of cuticular transpiration to total transpiration.

When plants first begin to experience drought stress, they decrease the leaf water loss mainly by decreasing the leaf transpiration rate through stomatal closure. Conflicting reports exist in the literature regarding the impact of Si on leaf transpiration rate. Maize leaf transpiration is reported to be decreased by Si in the studies of Gao et al. (2004, 2006) and Amin et al. (2014). Liu and Guo (2013) reported that Si application reduced both the transpiration rate and stomatal conductance but had no effect on photosynthetic rate in alfalfa under drought stress. Although, it has been reported that Si reduced the excessive leaf transpiration in rice under normal growth conditions (Savant et al., 1996; Agarie et al., 1998; Ming et al., 2012), the results of Chen et al. (2011) and Ming et al. (2012) showed that rice leaf transpiration was enhanced by Si when the plants were experiencing drought. Many other results on drought stressed plants have been shown to be consistent with enhanced leaf transpiration by Si application (Hattori et al., 2005; Sonobe et al., 2009; Chen et al., 2011; Gong and Chen, 2012; Pereira et al., 2013; Zhang et al., 2013; Liu et al., 2014; Saud et al., 2014; Kang et al., 2016). Under salt stress, the leaf transpiration rate has also been widely reported to be enhanced by Si (Yeo et al., 1999; Parveen and Ashraf, 2010; Liu et al., 2015; Wang et al., 2015; Mahmood et al., 2016; Qin et al., 2016). Also relevant are the findings of Chen et al. (2016), who reported that Si application enhances the transpiration of sorghum experiencing K-deficiency. Therefore, we conclude that Si application generally enhances transpiration of plants under various conditions of water stress.

Silicon Enhances Root Water Uptake Under Stress Conditions

During water deficiency, the regulation of root water uptake, in some cases, may be more crucial to overcome stress injury than the regulation of leaf water loss (Aroca et al., 2012). Compared with the effect of Si on the leaf transpiration, fewer studies have focused on the impact of Si on root water uptake. Root water uptake capacity is represented by root hydraulic conductance (Steudle, 2000). Recently, improving root hydraulic conductance by Si application has been directly demonstrated in sorghum (Hattori et al., 2007; Sonobe et al., 2009, 2010; Liu et al., 2014), rye (Hattori et al., 2009), tomato (Shi et al., 2016), and cucumber (Wang et al., 2015; Zhu et al., 2015) under drought stress, salt stress and K deficiency conditions.

The extent of root hydraulic conductance depends on the driving force, root surface area, root anatomy, and root’s permeability to water (Steudle, 2000; Vandeleur et al., 2009; Sutka et al., 2011). A promotion of osmotic driving force by Si application has been observed in various studies. Sonobe et al. (2010) suggested that Si application leads to a strong water potential gradient through accumulation of soluble sugars and amino acids in the plant. A similar consequence of Si application was observed in rice (Ming et al., 2012) and canola (Habibi, 2014) under drought stress. Liu et al. (2015) reported that Si had no effect on osmotic potential of root xylem sap under osmotic stress although it increased root hydraulic conductance in sorghum (Liu et al., 2015). In the study of tomato under osmotic stress, water stress also did not cause the change in root osmotic potential in Si-treated plants (Shi et al., 2016). Under salt stress, Zhu et al. (2015) found that Si decreased root xylem osmotic potential via accumulation of soluble sugars in cucumber. Under K deficiency condition, Si was also seen to decrease the root xylem osmotic potential through accumulation of K in sorghum (Chen et al., 2016). Therefore, under those conditions, regulation of the osmotic driving force could play a central role in Si-mediated enhancement of water uptake.

In addition to driving force, aquaporins were reported to play a central role in regulating root water permeability in response to short term water stress (Maurel et al., 2008). Liu et al. (2014) firstly reported that Si-pretreatment significantly increased the expression of aquaporin genes, which in turn increased the root water uptake in sorghum under drought stress. Recently, Liu et al. (2015) and Zhu et al. (2015) also observed that Si application increased aquaporin expression in sorghum and cucumber under salt stress. In addition, Si can also increase aquaporin expression in sorghum under K deficiency (Chen et al., 2016). However, the expression of aquaporin genes was not significantly regulated (less than twofold) by Si application in tomato under water stress (Shi et al., 2016). It is worth noting here that only three aquaporin genes (SlPIP1; 3, SlPIP1; 5, and SlPIP2; 6) were studied in this tomato study. Furthermore, modulation of aquaporin transport activity can also occur at post-transcriptional level. It is speculated that increased root hydraulic conductance by Si under stress conditions may be partly ascribed to Si-induced reductions in oxidative stress and membrane damage (Li et al., 2015; Shi et al., 2016). Similarly, Liu et al. (2015) suggested that Si could enhance aquaporin activity by reducing H2O2 accumulation. Certainly, it can be concluded that regulation of aquaporin transport activity is involved in Si-induced enhancement of root hydraulic conductance under stress conditions. But whether it is a general mechanism for the enhancement of root hydraulic conductance under stress conditions requires further study.

When long-term water stress occurred, changes in root surface and anatomy may also be important for enhancing plant water uptake (Javot and Maurel, 2002). Under drought stress, Si-pretreatment has been reported to increase the root/shoot ratio, contributing to a higher ability of water uptake in sorghum (Hattori et al., 2005, 2009). The increased root/shoot ratio was also observed in other studies of sorghum (Ahmed et al., 2011a,b) and rice (Ming et al., 2012) under drought stress as well as cucumber under salt stress (Wang et al., 2015). These results suggest that Si-mediated modifications of root growth may also account for the increase in the water uptake ability in Si-treated plants. However, Liu et al. (2014) did not observe any Si-mediated changes in vessel diameter or vessel number of sorghum root under drought stress. And several researchers observed no effect of Si on the root/shoot ratio in other plant species under stress conditions (Gong et al., 2003; Gao et al., 2004; Sonobe et al., 2009; Chen et al., 2011; Shi et al., 2016). In summary, Si-mediated modification of root growth may enhance root water uptake under stress conditions, but this adjustment is not a common phenomenon to all plants and it remains unclear whether Si is directly involved in the modification of root growth or not. Further studies are needed to clarify how Si regulates root development under water deficient condition.

Conclusion and Perspectives

Water deficiency is one of the major environmental factors limiting the growth of plants and the production of crops. The investigations described here showed that Si application moderates the plant hydraulic properties by increasing the root water uptake, but not by decreasing their water loss under water deficient condition. As illustrated in Figure 1, the potential key mechanisms involved in Si-mediated enhancement of plant root water uptake under water deficiency include: (1) enhancement of the osmotic driving force via active osmotic adjustment; (2) improvement of aquaporin transport activity at both transcriptional and post-transcriptional levels; (3) modification of root growth and increasing root/shoot ratio (Figure 1).

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FIGURE 1. Possible mechanisms for silicon (Si) mediated water balance of plants experiencing water deficiency. (1) Si enhances the aquaporin activity by up-regulating the expression of Plasma membrane Intrinsic Protein (PIP) aquaporin genes and alleviating the ROS (reactive oxygen species)-induced aquaporin activity inhibition. (2) Si enhances the accumulation of soluble sugars and/or amino acids in the xylem sap by osmorugulation; Si activates the K+ translocation to xylem sap by the activation the expression of SKOR (Stelar K+ Outward Rectifer) gene. The osmolyte accumulations in the xylem sap increase the osmotic driving force. (3) Si might adjust the root growth and increase root/shoot ratio, which together with enhancement of aquaporin activity and osmotic driving force contribute to the improvement of root hydraulic conductance. The higher root hydraulic conductance results in increased uptake and transport of water, which helps to maintain a higher photosynthetic rate and improve plant resistance to water deficiency.

Predictions of future global environmental changes point to an increase in both the severity and frequency of water stress in the near future. Therefore, genetic and biochemical manipulation of crops to increase their capacities of Si absorption, translocation and distribution from applied Si fertilizer should be considered as a preferable choice to improve crop production under water deficient condition. However, the mechanisms behind the beneficial effects of Si are still largely unknown. Hence, the mechanisms by which Si moderates the plant water status still need further investigation, especially regarding the molecular and biochemical basis by which Si regulates plant water uptake. In addition, the application of Si and its performance under field conditions still needs extensive investigation.

Author Contributions

SW and DC wrote the manuscript. LY and XD helped in drafting the manuscript.

Funding

This study was supported by the National Natural Science Foundation of China (Grant No. 31101597), National Key Technology Support Program of China (Grant No. 2015BAD22B01), and 111 Project of Chinese Education Ministry (Grant No. B12007).

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Keywords: silicon, water status, water balance, drought, salt stress, transpiration, water uptake

Citation: Chen D, Wang S, Yin L and Deng X (2018) How Does Silicon Mediate Plant Water Uptake and Loss Under Water Deficiency? Front. Plant Sci. 9:281. doi: 10.3389/fpls.2018.00281

Received: 13 December 2017; Accepted: 19 February 2018;
Published: 05 March 2018.

Reviewed by:

Steven Jansen, University of Ulm, Germany
Walter Chitarra, Consiglio per la Ricerca in Agricoltura e l’Analisi dell’Economia Agraria (CREA), Italy
Dimitrios Savvas, Agricultural University of Athens, Greece

Copyright © 2018 Chen, Wang, Yin and Deng. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Shiwen Wang, [email protected]

Sours: https://www.frontiersin.org/articles/340168

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Root water uptake and its pathways across the root: quantification at the cellular scale

Abstract

The pathways of water across root tissues and their relative contribution to plant water uptake remain debated. This is mainly due to technical challenges in measuring water flux non-invasively at the cellular scale under realistic conditions. We developed a new method to quantify water fluxes inside roots growing in soils. The method combines spatiotemporal quantification of deuterated water distribution imaged by rapid neutron tomography with an inverse simulation of water transport across root tissues. Using this non-invasive technique, we estimated for the first time the in-situ radial water fluxes [m s−1] in apoplastic and cell-to-cell pathways. The water flux in the apoplast of twelve days-old lupins (Lupinus albus L. cv. Feodora) was seventeen times faster than in the cell-to-cell pathway. Hence, the overall contribution of the apoplast in water flow [m3 s−1] across the cortex is, despite its small volume of 5%, as large as 57 ± 8% (Mean ± SD for n = 3) of the total water flow. This method is suitable to non-invasively measure the response of cellular scale root hydraulics and water fluxes to varying soil and climate conditions.

Introduction

Roots serve a vital role (among other functions) in extracting water from the soil, transporting it to the shoot and sustaining transpiration. To fulfill this function, roots have a complex anatomical structure consisting of different cell layers with varying hydraulic conductivities. This composite structure offers different pathways for radial flow of water from the root surface towards xylem vessels1,2,3: (i) the apoplastic pathway through the intercellular space and the cell wall; (ii) the symplastic pathway via plasmodesmata channels extending across neighboring cells; and (iii) the transcellular pathway which involves crossing membranes of neighboring cells. Pathways (ii and iii) are commonly referred to as the cell-to-cell pathway.

Roots are capable of varying the permeability of their cells and tissues to fulfill multi-facet functions, such as (i) transport of water and nutrients towards the xylem vessels; (ii) protection against desiccation in drying soils; and (iii) avoidance of leakage of nutrients and photosynthesized compounds into the soil4,5,6. For instance, the permeabilities of the endodermis and exodermis are reduced by the deposition of Casparian bands and suberin lamellae2,7,8. These modifications are located perpendicular to the radial direction and external to the plasma membrane and reduce the permeability of the apoplastic pathway, i.e., they act as gatekeepers9,10. Another important factor affecting root permeability is the expression of aquaporins across the cell membrane11,12,13,14. Aquaporins are water channels across cell membranes and their expression and activity (opening and closure) affect the cell permeability and that of the whole root tissue.

The relative contribution of the apoplastic and cell-to-cell pathways to the total water flow across the root tissue is still a matter of debate2,15,16. The large size of intercellular space within the root cortex (ca. 100–320 µm) and the small width of primary cell walls (ca. 3–30 nm) suggest that the permeability of the apoplastic pathway should be much greater than that of the cell-to-cell pathway (via crossing the cell membrane or plasmodesmata)17,18,19. This argument suggests a prevalent contribution of the apoplastic pathway. An alternative argument, however, suggests that the apoplastic pathway partly impedes the flow of water19,20,21 because the apoplast has a small cross-sectional area (ca. 3% in maize) and it is tortuous. In addition, endodermis and exodermis interrupt the continuity of the apoplastic pathway, forcing water to flow through the cell-to-cell pathway. However, some studies claimed that the apoplastic barriers are imperfect and have a varying degree of interruption depending on plant species, developmental stages and growth condition22,23,24. Therefore, both pathways appear possible, but their relative contributions remain unanswered.

The relative importance of apoplastic and cell-to-cell pathways has been indirectly estimated based on measurements of root/cell hydraulic conductance using a root/cell pressure probe after inducing osmotically or hydrostatically driven water flow across the root tissue25,26,27. The idea was that hydrostatically driven flow does not distinguish between parallel apoplastic or cell-to-cell pathways - i.e. an increase in hydrostatic gradient equally increases the water flow through the apoplastic and cell-to-cell pathways. In contrast, an osmotically driven flow generates flow only across the cell-to-cell pathway, where selective membranes are involved. The importance of the two pathways on the total conductance of the root is estimated by comparing the two measurements. An alternative is to measure the hydraulic conductance of roots before and after blocking the apoplastic or the cell-to-cell pathways15,28. However, these methods are restricted to excised roots (invasive) and do not allow to resolve the local flow field of water across the tissue; for instance, even if the apoplast is completely blocked at the endodermis, it is not clear how much water flows through the apoplast inside the cortex. Direct identification and quantification of flow pathways require a method to measure the flow field of water across the root tissue under realistic conditions.

Motivated by the lack of experimental and non-invasive techniques, we developed an in-situ method to resolve the spatial distribution of water fluxes across the root tissue. The method consists of visualizing and quantifying the radial transport of deuterated water (D2O, as a tracer of normal water) inside the root of growing plant in soil using a rapid neutron tomography technique. In our previous works, we used neutron radiography to visualize in two-dimensions the transport of D2O inside the roots of plants growing in soil29. A diffusion-convection model including different pathways of water was developed to quantify the transport of water across the root tissue30. Relying on two-dimensional information, the radial flux of water was successfully quantified, but the model was not sensitive to the pathways of water across the root tissue. In other words, we could estimate the total flow of water entering the root in a given location, but we could not resolve the flow field across the root tissue and discriminate between apoplastic and cell-to-cell pathways. Recent advancements in neutron imaging allow for much faster scanning times31,32,33. When used for the water uptake investigations, Zarebanadkouki et al.32 performed on-the-fly neutron tomography with a scanning time of 180 seconds and Tötzke et al.33 used scanning time of 10 seconds, to successfully visualize D2O entering the root system in three-dimensions. However, it is not yet clear whether and how such information could be used to predict the pathways of water across the roots. Here, we tested: (i) whether series of rapid neutron tomographies (single tomography scanning time of 30 s and a voxel size of 45 µm) is capable of resolving gradients in D2O concentration along the radial distance to the root center, and (ii) whether we could inversely calculate the relative importance of the two pathways by using a model of D2O transport that explicitly considers the apoplastic and cell-to-cell pathways.

Results

An exemplary reconstructed 3D root system of a twelve days old lupin before D2O injection is shown in Fig. 1a. This image was reconstructed from 180 projections uniformly distributed over angular views of 0 to 179 degrees. D2O was injected at the soil surface while the root system was simultaneously imaged at varying angles for a period of two hours. Exemplary transverse sections at a depth of 5 cm from the soil surface illustrate the transport of D2O across the root tissue of two plants during the night (negligible transpiration) and daytime (illuminated with constant light intensity) (Fig. 1b,c and Supplementary Information Fig. S1). The tomograms clearly resolved gradients in the concentration of D2O across the root tissue and showed a steep drop in D2O concentration at the endodermis. The gradients were steeper during nighttime than daytime indicating that the presence of an endodermis significantly limited the transport of D2O across root tissue at night, while its effect was less pronounced during daytime measurements.

(a) Reconstructed root system of a twelve-days old lupine before D2O injection. D2O was injected in soil and its redistribution within soil and roots was monitored using time-series neutron radiography. (b,c) Transverse section of neutron tomographs showing the concentration of D2O (fraction of D2O to total water) across the taproot of two exemplary plants at different times after D2O injection during the night (b) and the daytime (c). The transverse sections are taken at a depth of 5 cm from the soil surface. The given time (t) refers to the time after D2O injection. These images show that the transport of D2O across the root tissue was faster during daytime than nighttime. During nighttime, D2O transport was significantly slowed down by the presence of endodermis. Detailed time-series neutron tomographs are shown as Supplementary Information Fig. S1. Note that these transverse sections show only the concentration of D2O in root and not the one in soil.

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We quantified the reconstructed neutron tomograms to calculate profile of D2O concentration across the root tissue (Fig. 2). The profiles showed steeper gradients in the concentration of D2O across the root tissue during nighttime while they were relatively flat shortly after D2O injection during the daytime. The concentration of D2O was averaged across regions with a size of three voxels at the outermost parts and innermost parts of roots (iso distance bands from the surface) to represent the average D2O concentration in the root cortex and the root stele, respectively (Fig. 2c,d). Our results showed: (i) a faster increase in the concentration of D2O in both root cortex and root stele during daytime than nighttime, and (ii) a steeper gradient in concentration of D2O between cortex and stele during nighttime compared to daytime.

Exemplary profiles of D2O concentration (fraction of D2O to total water) as a function of distance from the root center at selected times after immersion in D2O during nighttime (a) and daytime (b). D2O concentration (fraction of D2O to total water) in the stele and root cortex as a function of time after immersion in D2O during nighttime (c) and daytime (d). Root stele and the root cortex represent the average concentration in the third innermost and third outermost voxels of the roots. In all figures, the circles show concentrations obtained from image processing of CTs and the lines show the best-simulated profiles of concentration. In (a,c) the region shown with a grey color indicates the position of endodermis.

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The transport of D2O into roots during the night was described by a diffusion equation and its transport during the day was described by a hydraulic model coupled with the convection-diffusion equation (Eq. 6). These equations were solved numerically for a conceptualized flow domain across the root tissue (Fig. 3). The model reproduced the measured profiles of D2O concentration during both nighttime and daytime (Fig. 2). We estimated the parameters (cell scaled diffusion coefficients and hydraulic conductivities of root tissue) from an independent inverse simulation of D2O transport measured in three plants during nighttime and three plants during daytime conditions (the parameters are given as Supplementary Information in Fig. S2).

(a) Light transmission microscopy of a cross-section of the taproot of a twelve-days old lupin at a distance of 5 cm from the soil surface. (b) Neutron tomograms showing a cross-section of the taproot. The color is proportional to the water content. (c) Radially symmetric root schematization. (d) The fraction of the root tissue used to set-up the model with one apoplastic pathway indicated in blue. (e) Schematic of the flow domain across the root tissue. (f) Discretization of the flow domain into 2000 finite elements.

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The solution of the inverse problem gave the spatial distribution of water flux across the root tissue (qr, Eq. (10), Fig. 4a). The radial flux [m s−1] was greater in the apoplastic pathway than in the cell-to-cell pathway. The profiles of radial flux at a distance of 0.06 cm from the root center are plotted in Fig. 4b. At this location, the radial flux of water through the apoplastic pathway was 104 ± 73 times higher in the apoplastic pathway than in the cell-to-cell pathway (Mean ± SD for n = 3) (Fig. 4c). This ratio was rather similar across the root tissue except at the endodermis where the apoplastic pathway was blocked17,18,19. Despite a higher radial flux through the apoplastic pathway, the overall contribution of this pathway in the transport of water (Qr,apo. Eq. 9) was rather similar in both pathways (Fig. 4d). At this selected location the apoplastic pathway contributed to 57 ± 8% (Mean ± SD for n = 3) of total water taken up by roots.

(a) Color mapped distribution of radial flux across the root tissue (qr, Eq. 10). (b) A transverse profile of radial flux in the cortex at a distance of 0.06 cm from the root center (position indicated by two arrows in subplot a). (c) Average flux across the apoplastic and cell-to-cell pathways. (d) The total flow of water across the apoplastic and cell-to-cell pathways (Qr,apo and Qr,cell, Eq. 9). Note that in (b,c) the data are averaged across three plants and are referring to a position with a distance of 0.06 cm from the root center in the root cortex. The error bars show the standard deviation.

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To simulate the transport of D2O across the root tissue three parameters were needed to describe the transport of D2O into the root via diffusion during nighttime and four parameters to describe the convective transport of D2O into the roots during the daytime (see Materials and Methods). It was further assumed that diffusion coefficients did not vary between day and night. We evaluated the sensitivity of the model to the parameters by changing the best-obtained parameters by a factor of 0.2 to 5 for the night measurement and by a factor of 0.1 to 10 for the day measurement and for each of the 25000 runs we calculated the predefined objective function (Eq. 14) (Supplementary Information Figs S3 and S4).

A global minimum was reached with multiple sets of diffusion coefficients and hydraulic conductivities, indicating that the inverse problem can be solved with different sets of cell-scaled hydraulic properties of the root tissue (Figs S3 and S4). Although the model was not sensitive to all hydraulic parameters, it was sensitive to the water fluxes. The sensitivity analysis showed that a global minimum was reached around the total radial flow and the ratio of apoplastic and cell-to-cell water flow which gave the best fit with rather small uncertainty (Fig. 5). This proves that the model was sensitive to both, the total radial flow of water and the ratio between the apoplastic and the cell-to-cell flow. We conclude that the proposed method allows the estimation of the relative contribution of the apoplast and cell-to-cell pathways to the transport of water across the root cortex.

Sensitivity analysis of the model to the magnitude and spatial distribution of water fluxes across the root tissue. The hydraulic conductivity of different parts of the root tissue (apoplast, cell membrane, endodermis, and protoplast) was multiplied by a factor ranging from 0.1 and 10 and their effects on the objective function (Eq. 14), flow of water into the roots (Qr = Qr,apo + Qr,cell, Eq. 9), and the relative contribution of the apoplast to (FQr,apo, Eq. 11) were calculated. The colormap shows the value of the objective function as a function of the estimate Qr and FQr,apo. The sensitivity analysis was performed around the optimal solution of fitted hydraulic conductivities. Two parameters were changed simultaneously while the others were kept constant. Note that the fluxes are shown here with a subscripted star which refers to the normalized values relative to their optimal fitted value.

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Discussion

Our novel method combines experiments and modeling to quantify the fluxes of water across the root tissue. Rapid neutron tomography was used to in-situ trace the transport of D2O in roots. The imaged concentrations of D2O were inversely simulated to solve a diffusion-convection equation for the composite transport of water across the root tissue. The solution of the inverse problem gave the cell-scaled water fluxes. The novelty of this study was to quantify the spatial distribution of D2O over time in three dimensions and to estimate relative contributions of different pathways of water across root tissues. A recent method for in-situ quantification of water fluxes along the root system of transpiring plants29,30 was the starting point to quantify the relative importance of different pathways of water across the root tissue. The former method allowed to estimate the total radial flow, but it could not solve the relative importance of the apoplastic and cell-to-cell pathways. The main challenge was to push the limit of neutron tomography towards a much faster data acquisition while maintaining a high spatial resolution. This was successfully accomplished at the imaging station ICON at Paul Scherrer Institute. The imaging facilities allowed us to resolve the transport of D2O across the root tissue at a voxel size of 45 µm and with a tomography acquisition time of 30 s. Gradients in D2O concentration across the root tissue were visible and, by means of an inverse modeling approach, the contribution of the different pathways in the transport of water across the root tissue was quantified.

To quantify the flow field of water across the root tissue, we used a simplified domain consisting of a cell-to-cell pathway encapsulated by a thin layer representing the cell membrane and the remaining inner space representing the cell protoplast. The apoplastic pathway was considered as a flow domain between the cell membrane of neighboring cells. The cell-to-cell pathway was assumed to be a continuous flow domain interrupted at the endodermis, which had a cell membrane with lower specific permeability than the rest of the cells. Similarly, the apoplast was assumed to have a straight shape. In reality, the cell-to-cell pathway consists of several layers of cells, with water flowing from cell to cell by crossing two adjacent cell membranes or through plasmodesmata. Additionally, the apoplast is not straight but rather tortuous and with three-dimensional geometry. Explicitly solving the flow model in the exact cell geometry, including the plasmodesmata and the complex three-dimensional architecture of the apoplast, would introduce a large number of parameters that are not easily extractable. Therefore, to reduce the number of unknowns, we kept the flow domain as simple as possible, but complex enough to include the two pathways, their exchange and the role of the endodermis. Recently, Couvreur et al.3 developed a hydraulic model that computes the convective flow of water through walls, membranes, and plasmodesmata of each individual cell throughout a complete root cross section. Including the diffusive transport of D2O in such a model is a promising development of our method to reveal cell-scaled hydraulic properties of roots and the water fluxes across the root tissue including its anatomical complexities. Another important assumption of our model is that the diffusion coefficients did not vary between day and night. However, the diffusion coefficient of the membrane might change in response to varying aquaporin activity. Additionally, water flow is likely to increase the dispersivity of D2O through the apoplast, resulting in a greater apparent diffusivity of the apoplast. As the apoplast is more diffusive than the endodermis and as convection is dominant through the apoplast (during daytime), it is unlikely that assuming constant diffusivity in the apoplast impacts our result. Independent measurements of water during the experiments might allow for releasing the assumption that the diffusion coefficients are constant and for studying how these coefficients vary over time and soil and climate conditions.

Our results showed that the apoplast, despite occupying a small fraction of the root cross section (ca. 5%), contributes to half of the transport of water flowing across the cortex (57 ± 8% of total water taken up by the roots). This is explained by the higher hydraulic conductivity of the apoplast compared to the cell-to-cell pathway. This result is in agreement with the literature. For instance, apoplastic conductance of cherry seedlings was 57% of the total root conductance, suggesting that its contribution in overall root water uptake would be 57%26. A similar result was reported for lupin roots grown in soil by Bramley et al.15, who reported that in a narrow-leafed lupin water moves predominantly through the apoplastic pathway. However, those measurements refer to the contribution of the apoplast to the total hydraulic conductance, while our data refer to the flow rates across both apoplastic and cell-to-cell pathways. So, this information is not comparable in a straightforward way.

The hydraulic model coupled with the diffusion-convection equation was needed to solve the inverse problem and estimate the distribution of water fluxes across the root tissue. The model required a large number of parameters and to some of these parameters, such as the hydraulic conductivities, the model was not sensitive. (Supplementary Information Figs S3 and S4). Additionally, the estimated values of these parameters, in particular, the hydraulic conductivities of the different pathways, are affected by the simplified geometry of the flow domain. Therefore, the estimation of the cell-scale hydraulic conductivities and diffusion coefficients has a high degree of uncertainty. However, independently from these parameters, the inverse problem was sensitive to the total flow of water and to its spatial distribution. The sensitivity analysis showed that both, the total radial flow and the relative importance of apoplast and cell-to-cell, were estimated with high accuracy. Therefore, compared to the uncertainty in the cellular-scale hydraulic and diffusion parameters, the estimation of water fluxes is accurate.

In conclusion, the proposed method allows to non-invasively quantify the fluxes of water across the root tissue and the relative importance of their pathways. We found that fluxes are not uniform, with the flux in the apoplast being much higher than across the cell-to-cell, and with the endodermis partly or completely blocking the apoplastic pathway. This method offers new opportunities to answer long-standing open questions: for instance, it could be used to test whether and to what extent (i) root permeability varies with soil drying and transpiration, and (ii) varying root permeabilities is an adaptive root trait in response to water stress. Such alterations of root permeabilities might take place at the endodermis, due to suberization, or due to varying aquaporin activity of all root cells or of some of them. The information provided by a rapid neutron tomography could help to answer such open questions in root-soil water relations.

Materials and Methods

Plant and soil preparation

We grew six lupines (Lupinus albus L. cv. Feodora) in graphite cylindrical containers (a diameter of 27 mm and a length of 100 mm) filled with a sandy soil collected from an Ap horizon. The soil consisted of 73% sand, 18% silt, and 9% clay. The containers were filled vertically by pouring the dry soil through a 2 mm sieve, which resulted in a homogeneous packing. The seeds were soaked for 10 minutes in 10% H2O2 solution, were thoroughly washed, and then were let germinate on filter paper in a 10 mM L−1 CaSO4 solution for 48 h. One seedling was sown in each container at a depth of 1 cm. The plants were kept in a climate chamber under controlled conditions during the whole growth period: day-night temperature of 24–19 °C, the daily light cycle of 14 hours, the light intensity of 500 µmol m−2 s−1 at the top of the canopy, and relative humidity of 60%. When the plants were three days old, a one-centimeter layer of quartz gravel (grain diameter of 1.7–2 mm) was added on the top of the soil to minimize evaporation. Plants were irrigated by capillary rise every fourth day.

The neutron tomography measurements started when the plants were 12 days old. Prior to the measurement, plants were placed in the imaging station ICON at the Paul Scherrer Institute and transpiration was monitored gravimetrically by weighing the samples at two different times with an interval of three hours. Daytime transpiration was 0.65 ± 0.13 cm3 h−1 and it was negligible at night-time. Three plants were tomographed at daytime and the other three at nighttime.

Neutron tomography

Sequences of neutron tomographies allow for imaging the spatiotemporal distribution of water in soil and plants34,35. A parallel neutron beam crosses the sample while the sample is continuously rotated at constant angular velocity while the neutron detector acquires projections at a given frame rate. The transmitted neutron beams at each angle carry information about the composition and thickness of the sample. 3D volumetric information is obtained by reconstructing the projections taken at different angular views using the filtered back projection algorithm36.

Neutron tomography was performed at the beamline ICON at Paul Scherrer Institute (PSI), Villigen, Switzerland. The sample was fixed on a rotational stage whose axis of rotation was placed at about 20 mm upstream the scintillator screen. The rotational stage together with the negligible readout time of sCMOS detector available at ICON beamline allowed for the application of a high temporal resolution on-the-fly tomography. The sCMOS detector was set to acquire images of 1200 pixels in width and 2560 pixels in height, the exposure time was set to be 0.1667s (6 frames/s), the rotation rate of the sample stage was set at 0.5°/s and the proton current at the SINQ target was about 1.43 mA, which corresponds to a neutron flux of approximately 1.25 × 107 neutrons cm−2 s−1 at the sample position. The temporal resolution of the series of neutron tomographies (from the angular view of 0° to 179°) was 30 s. While the nominal pixel size of the acquired images corresponded to 45 µm, the spatial resolution in 2D is about 200 μm/line pair as assessed visually from the image of PSI test pattern Gd-based Siemens star37. The detector system allowed an on-the-fly tomography consisting of 7000 projections. The experiments were performed in such a way that 762 projections were acquired before D2O injection. Then, D2O was injected into the sample from the top using a remotely controlled in-situ titration system38 while the sample was simultaneously tomographed.

Image processing

A set of parallel beam projections obtained from angular views in the interval 0° to 179° were reconstructed into a 3D volume using a filtered back projection algorithm in Matlab 2015b. Prior to the reconstruction, neutron radiographs were normalized using the Beer-Lambert law for flat field (I0), dark current (Idc) and beam variation over time as follows

$$\tau =-\log (\frac{I-{I}_{dc}}{{I}_{0}-{I}_{dc}}\times \frac{{D}_{0}}{D})$$

(1)

where τ is the optical thickness (sample thickness × attenuation coefficient) [−], I is the grey level proportional to the attenuated neutron beam [# neutrons], I0 is the grey level proportional to the incident neutron beam [# neutrons], D and D0 are scalar values proportional to the neutron dose. They are estimated as the average value from an image region outside the sample projection through all the radiographs and flat field, respectively.

After normalization, the projections were rearranged into sinograms, i.e. images containing the information required to a reconstruct a single slice. The resulted sinograms were first filtered to remove ring artifacts39 and then back projected using the iradon function in Matlab to reconstruct transverse slices of the sample. The iradon function in Matlab assumes that the center of rotation is the center point of the projections. This function was modified to take a customized center of rotation with a subpixel precision (0.01 × spatial resolution of 2D images). The center of rotation with a subpixel precision was determined based on integral of absolute values of a reconstructed slice proposed by Donath et al.40.

The reconstructed voxels represent the average attenuation coefficient of the materials present in the sample sub-volume at the position of the voxel. For voxels containing roots, this value can be described in terms of volumetric fractions of H2O, D2O, and soil as

$${\mu }_{tomo}(t)={F}_{H2O}(t){\mu }_{H2O}+{F}_{D2O}(t){\mu }_{D2O}+{F}_{roottissue}{\mu }_{roottissue}$$

(2)

where μtomo(t) is the voxel-wise neutron attenuation coefficient at time t in the tomograms [cm−1], μH2O, μD2O and μroot tissue are the linear neutron attenuation coefficient [cm−1] of water, D2O, and root tissue (dry biomass of root), respectively, and F are their volumetric fractions [−]. Note that the sum of the volumetric fraction of the different phases is equal to one. Here it assumed that the contribution of dry root tissue in neutron attenuation is negligible compared to the contribution of the liquid phase (volumetric water content of the root is ca. 90%). The neutron attenuation coefficient describes the probability of neutron interactions with the materials per unit of thickness and was determined by neutron radiography of known thickness of each component (H2O, D2O, and root tissue).

It is assumed that volumetric liquid content of the root tissue did not change after immersion in D2O. Then the voxel-wise concentration of D2O in the voxel containing root can be calculated as

$$C=\frac{{F}_{D2O,root}}{{F}_{liquid,root}}$$

(3)

where

$${F}_{liquid,root}=\frac{{\mu }_{tomo}(t=0)}{{\mu }_{H2O}}$$

(4)

$${F}_{D2O,root}=\frac{{\mu }_{tomo}(t)-{\mu }_{tomo}(t=0)}{{\mu }_{D2O}-{\mu }_{H2O}}$$

(5)

where C is the concentration of D2O in roots (fraction of D2O to total water), Fliquid,root is the volumetric fraction of liquid (H2O + D2O) in the voxels containing root, FD2O,root is the volumetric fraction of D2O in the voxels containing root, t refers to the time after D2O injection and t = 0 is the time when D2O was injected. Equation 3 is only valid for calculation of D2O concentration in the roots and its assumption (volumetric liquid content of the root tissue did not change after D2O injection) is not be valid in soil. The concentration of D2O in soil was calculated from the neutron radiographs of the entire sample following the approach presented in Zarebanadkouki et al. (2013).

Modeling of D2O transport

The radial transport of D2O into the root is modeled using a diffusion-convection equation where diffusion depends on the concentration gradient across the root tissue and the diffusional permeability of the root tissue, and convection depends on the gradient in water potential between xylem and soil and the permeability of the root tissue29,30. The equation can be written as:

$$\theta \frac{\partial C}{\partial t}=\nabla (D\Delta C)-\nabla (qc)$$

(6)

where θ is the volumetric water content [cm3 cm−3], C is the concentration of D2O [cm3 cm−3], D is diffusion coefficient [cm2 s−1] of the root tissue, q is the flux of water [cm s−1], ∇ is the divergence in space and Δ is the gradient in space. The concentration of D2O over time was quantified through the neutron tomograms based on Eq. 3. The flux of water [q, cm s−1] can be computed as

where K is the hydraulic conductivity across the root tissue [cm s−1] and h the is water potential expressed in centimeter heads [cm]. Under steady-state conditions, the profile of water potential across the root tissue is computed by solving the equation below

$$0=-\,\nabla (K\Delta h)$$

(8)

With a known boundary condition (water potential in the soil and in the xylem) Eq. 7 gives the velocity of water across the root tissue (q) in the apoplastic and cell-to-cell pathways. The overall contribution of each pathway in root water uptake [cm3 s−1] depends not only on the radial flux of water, qr, through each pathway but also on their volumetric fraction. The total flow of water in each pathway was calculated as follows

$$\begin{array}{rcl}{Q}_{r,apo} & = & 2\pi r\overline{{q}_{r,apo}}{\omega }_{apo}\\ {Q}_{r,cell} & = & 2\pi r\overline{{q}_{r,cell}}{\omega }_{cell}\end{array}$$

(9)

where Qr,apo and Qr,cell are the total flow of water through the apoplastic and the cell-to-cell pathway per unit of root length, respectively [cm3 s−1 cm−1], r is the distance from the root center [r = 0.06 cm], \(\overline{{q}_{r,apo}}\) and \(\overline{{q}_{r,cell}}\) are the average radial flux [cm s−1] through the apoplastic and the cell-to-cell pathways, and ωapo and ωcell are the volumetric fractions of the apoplastic and cell-to-cell pathways. To do so, qr across the root tissue was calculated as follows

$${q}_{r}=\sqrt{{(K\frac{\partial h}{\partial x})}^{2}+{(K\frac{\partial h}{\partial z})}^{2}}$$

(10)

Here, x and z are the cartesian coordinates across the root tissue. The overall contribution of each pathway in the total flow of water was calculated as

$$\begin{array}{rcl}{F}_{Qr,apo} & = & \frac{{Q}_{r,apo}}{{Q}_{r,tot}}\\ {F}_{Qr,cell} & = & \frac{{Q}_{r,cell}}{{Q}_{r,tot}}\end{array}$$

(11)

where Qr,tot is the total flow of water across the root tissue and it is equal to Qr,apo + Qr,cell.

Model implementation and parameterization

For a quantitative description of water flow across the root tissue, we conceptualized the complex structure of the root tissue as shown in Fig. 3. The root cortex was represented as two cell-to-cell pathways that were surrounded by three apoplastic pathways in their longitudinal direction. Two apoplastic pathways were located on the sides of each cell-to-cell pathway and one apoplastic pathway was located between them. The middle apoplastic pathways had a double thickness as the ones on the sides taking into account that the apoplastic pathways on the sides are shared between their surrounding cell-to-cell pathways outside of the simulated domain. The apoplastic domain was assumed to occupy 5% of the root volume. Note that for computational reasons we had to take this volumetric fraction slightly bigger than the value reported by Fritz and Ehwald19 (ca. 3% for maize root). Each cell-to-cell pathway was encapsulated by a thin region (with a thickness of 1.3 µm) representing the cell membrane and the remaining inner space representing the cell protoplast and cell plasmodesmata. The apoplastic pathway was fully interrupted at a distance of 0.45 times the root radius from the root center (root radii of ca. 0.6–0.7 mm, obtained from root cross-section) by a distinct layer of cells (with the thickness of one cell) representing the root endodermis. Note that the endodermis was assumed to be one cell in thickness, surrounded by two membranes on its sides and the inner space of this cell was assumed to be similar to the protoplast of other root cells. The stele was represented by one cell-to-cell pathway that was encapsulated by the cell membrane and surrounded by two apoplastic pathways in its side in the longitudinal direction. The flow domain arrived till the xylem vessels.

The flow domain and cellular scale hydraulic information were combined to build a finite element model for simulating the transport of D2O across the root tissue. The flow domain was discretized into 2000 elements of varying size. Eq. 6 was numerically solved using an implicit finite element method in Matlab. To numerically solve this equation, we imposed Dirichlet boundary condition (known D2O concentration shown as Supplementary Information Fig. S5) at the root surface and Neumann boundary condition (zero flux) in the xylem. The diffusion coefficients [cm2 s−1] of the following domains were the parameters needed to solve Eq. 6: the apoplastic pathway (Dapoplast), the membrane of root cells (Dcell membrane), the membrane of endodermis (Dendormis), and the cell protoplast (Dprotoplast). To describe the convective transport of D2O across the root tissue, the flow field was first reconstructed by numerically solving Eqs 7 and 8. These equations were solved by imposing Dirichlet boundary condition (known water potential) in soil and xylem. The hydraulic conductivities [cm s−1] of the following flow domains were needed: apoplastic pathway (Kapoplast), the membrane of root cells (Kcell membrane), the membrane of endodermis (Kendodermis), and the cell protoplast (Kprotoplast). Note that the diffusion coefficients and hydraulic conductivities at the root-soil interface were scaled by the volumetric soil water content. Across the root tissue, the volumetric water content is around 100% while it was around 20% in the soil suggesting that only 20% of the root area will be available for D2O to flow into the root. For the membranes, the diffusion coefficients are related to the diffusional permeabilities (Pd with a unit of cm s−1) as following:

$$\begin{array}{rcl}P{d}_{cellmembrane} & = & \frac{{D}_{cellmembrane}}{{d}_{cellmembrane}}\\ P{d}_{endorermis} & = & \frac{{D}_{endodermis}}{{d}_{endodermis}}\end{array}$$

(12)

where dcell membrane and dendodemris are the thicknesses of the membrane of root cells and of the endodermis [cm], respectively. The hydraulic permeabilities of the membranes were also defined as

$$\begin{array}{rcl}L{p}_{cellmembrane} & = & \frac{{K}_{cellmembrane}}{{d}_{cellmembrane}}\\ L{p}_{endodermis} & = & \frac{{K}_{endodermis}}{{d}_{endodermis}}\end{array}$$

(13)

where is Lp is the membrane hydraulic permeability [cm cm−1 s−1 which is equivalent to 104 cm MPa−1 s−1].

The profiles of D2O concentration during nighttime were used to estimate the cell scaled diffusion coefficients of Dcell membrane, Dendodermis, and Dprotoplast by inversely solving Eq. 6 and adjusting these diffusion coefficients to best reproduce the measurements. To reduce the number of unknowns, Dapoplast was assumed to be one-sixth of the self-diffusion coefficient of D2O (D0 = 2.27 × 10−5 cm2 s−141). Published values of diffusion coefficients for different solutes in the apoplast range from 1/5 to 1/60 of the diffusion in pure water21,42,43,44. We took a value close to the highest one (1/6) because D2O is a neutral molecule with a rather similar molecular weight compared to normal water. Lower values are expected for bigger and charged molecules. Note that we assumed that the change in soil water potential (inducing a convective transport of D2O across the root tissue) after D2O injection was negligible. Assuming that diffusion coefficients were constant during day and night, the profiles of D2O concentration during daytime were used to estimate the hydraulic conductivities of Kapoplast, Kcell membran,Kendodemris and Kprotoplast by inversely solving the Eq. 6 and adjusting these conductivities to best reproduce the measurements. To solve the equation, the xylem and soil water potentials were needed. The water potential in the soil was ca. −100 cm (based on the measured water content and water retention curve). The xylem water potential was measured in plants grown in the same conditions and imposed to the same transpiration rate using the Scholander bomb45 and it was 4000 cm. At the peak of the transpiration, plant shoots were carefully cut with a sharp razor blade and placed in Scholander bomb exposing the cut end to the atmosphere. Then the pressure at which a drop of water was observed at the cut end of the stem was taken as the leaf water potential.

The inverse problem was solved by minimizing a predefined objective function using the ‘global optimization toolbox’ in Matlab (‘pattern search’ algorithm). The ‘pattern search’ algorithm finds the global minimum of a pre-defined objective function. This solver has the advantage of accepting lower and upper pre-defined boundaries for the solution. It also allows linear and non-linear constraints to the solution. The objective function (Obj) to be minimized was defined as the root mean square of relative differences between measured and simulated D2O concentration at different locations across the root tissue at different times.

$$Obj=\Vert \frac{{({C}_{i,j}^{mes}-{C}_{i,j}^{sim})}^{2}}{{({C}_{i,j}^{mes})}^{2}}\Vert $$

(14)

where \(\Vert \cdot \Vert \) refer to the norm of a matrix, \({C}_{i,j}^{mes}\)and \({C}_{i,j}^{sim}\)are the measured and simulated concentration of D2O at location i and time j, respectively. Here i refers to the distance from the root surface and j to the time after immersion in D2O.

Before any effort to solve an inverse problem, it is very important to find out whether the pre-defined objective function is sensitive to the parameters to be optimized. A sensitivity analysis was performed around the optimal solution of fitted diffusion coefficients and hydraulic conductivities. A local sensitivity analysis was carried out by simultaneously varying two selected parameters confirming the sensitivity of the model to its parameters.

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Acknowledgements

This work is based on experiments performed at the Swiss spallation neutron source SINQ, Paul Scherrer Institut, Villigen, Switzerland. We also thank Dr. Valentin Couvreur from the University of Louvain, Belgium for his helpful advice and discussions on various technical aspects of the modeling of water transport across the root tissue. We also thank Dr. Daniel Schlaepfer from the University of Yale for proofreading a former version of the manuscript and giving us his helpful comments and suggestions. Finally, we acknowledge three anonymous reviewers for their suggestions which improved the readability of the manuscript. This publication was funded by the German Research Foundation (DFG) and the University of Bayreuth in the funding program for Open Access Publishing.

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Affiliations

  1. Chair of Soil Physics, University of Bayreuth, Bayreuth, Germany

    Mohsen Zarebanadkouki, Faisal Hayat & Andrea Carminati

  2. Laboratory for Neutron Scattering and Imaging, Paul Scherrer Institut, Villigen, Switzerland

    Pavel Trtik & Anders Kaestner

Contributions

M.Z. and A.C. developed the experiments and the conceptualized the model used to quantify water transport across the root. M.Z. A.K. and P.T. conducted the neutron tomography experiments and analyzed the neutron images. M.Z. and F.H. conducted the Scholander bomb experiments. All authors reviewed and commented on the manuscript.

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Correspondence to Mohsen Zarebanadkouki or Anders Kaestner.

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Zarebanadkouki, M., Trtik, P., Hayat, F. et al. Root water uptake and its pathways across the root: quantification at the cellular scale. Sci Rep9, 12979 (2019). https://doi.org/10.1038/s41598-019-49528-9

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  • Three-dimensional in vivo analysis of water uptake and translocation in maize roots by fast neutron tomography

    • Christian Tötzke
    • , Nikolay Kardjilov
    • , André Hilger
    • , Nicole Rudolph-Mohr
    • , Ingo Manke
    •  & Sascha E. Oswald

    Scientific Reports (2021)

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Sours: https://www.nature.com/articles/s41598-019-49528-9
Animation 10.3 Absorption of water in plants

Plant water uptake from soil through a vapor pathway

Water uptake from the soil via a vapor pathway was tested. Viburnum suspensum L. plants were divided into: (1) irrigated, (2) drought with vapor and (3) drought without vapor treatments. Each plant was placed into a larger bucket containing deuterium-labeled water as a vapor source (vapor treatment) or no water (drought and irrigation treatments). We also tested whether uptake via a vapor pathway could mitigate drought effects. Net CO2 assimilation (A), transpiration (E) and stomatal conductance (gs) were measured daily until the first visible signs of stress. Soil water content, stem water potential (Ψ) and the stable hydrogen isotope ratio (δ2 H) of soil and plant xylem water were then measured in all treatments. We show that water is taken up by plants through the vapor phase in dry soils. The δ2 H values of the soil water in the vapor treatment were highly enriched compared to the background isotope ratios of the non-vapor exposed irrigated and drought treatments. Stem water δ2 H values for the vapor treatment were significantly greater than those for irrigation and drought treatments not exposed to isotopically enriched vapor. In this experiment, movement of water to the plant via the vapor phase did not mitigate drought effects. A, E, plant Ψ and gs significantly decreased in the drought and vapor treatments relative to the controls, with no significant differences between vapor and drought treatments.

Sours: https://pubmed.ncbi.nlm.nih.gov/32700800/

Water uptake plant

Plant Water Uptake in Drying Soils

Abstract

Over the last decade, investigations on root water uptake have evolved toward a deeper integration of the soil and roots compartment properties, with the goal of improving our understanding of water acquisition from drying soils. This evolution parallels the increasing attention of agronomists to suboptimal crop production environments. Recent results have led to the description of root system architectures that might contribute to deep-water extraction or to water-saving strategies. In addition, the manipulation of root hydraulic properties would provide further opportunities to improve water uptake. However, modeling studies highlight the role of soil hydraulics in the control of water uptake in drying soil and call for integrative soil-plant system approaches.

The fundamental mechanism of water flow in plants has been described for many years (Steudle, 2001). Briefly, the diffusion of vapor through stomata leads to the evaporation of water from the surface of inner leaf tissues and an increase of tension in the xylem that propagates to each root segment following the cohesion-tension principle (in this context, a root segment can be seen as a portion of root with uniform hydraulic properties). Where this tension is higher than the surrounding soil, it induces an inflow of water from the rhizosphere, following paths of low soil hydraulic resistance. How far plants are able to sustain their leaf water demand is therefore largely dependent on the hydraulic properties of the soil-root system.

The spatial geometry of the root system is typically considered as a major determinant of water availability, essentially because the placement of roots in the soil domain delineates the extent of soil exploration and sets an upper limit to the volume of potentially accessible water (Fig. 1A). The level of details required to link the volume of accessible water to the spatial geometry of the root system depends on crop species, sowing patterns, and soil hydraulic properties. While a vertical profile of root density is generally sufficient for crops sown at very high densities in a highly conductive soil, two- or three-dimension descriptions are needed for crops with large interrows or in water-depleted soils (Couvreur, 2013).

Figure 1.

Properties of the soil-root system. A, Spatial geometry of the root system. B, Root hydraulic architecture is the integration of axial (orange lines) and radial (blue lines) hydraulic resistances of individual root segments (gray circles) and soil elements (brown circles). C, Soil water content distribution (white indicates dry and blue indicates wet).

Figure 1.

Properties of the soil-root system. A, Spatial geometry of the root system. B, Root hydraulic architecture is the integration of axial (orange lines) and radial (blue lines) hydraulic resistances of individual root segments (gray circles) and soil elements (brown circles). C, Soil water content distribution (white indicates dry and blue indicates wet).

Within the volume of soil explored by a root system, however, water uptake is unevenly shared among root segments. Individual segments differ by their axial and radial hydraulic conductivities and by the conductance of the shortest paths that links them to the shoot base. These properties, encapsulated in the concept of root hydraulic architecture (Fig. 1B), have a large impact on the hydraulic conductance of the root system and, together with the soil hydraulic status, on the distribution of water capture among individual root segments. Consequently, sites of higher uptake occur throughout the root zone and contribute to the heterogeneous spatial distribution of the plant-available soil water availability (Doussan et al., 2006). For a given root, these preferential sites are predicted a few centimeters from the root tip, where protoxylem and xylem elements are conductive and hydrophobic structures are lacking. This was recently confirmed experimentally by neutron radiography experiments (Zarebanadkouki et al., 2013).

The distribution and amount of water uptake in the root zone is also influenced by the distribution and amount of the available soil water (Fig. 1C). As the soil matric potential and hydraulic conductivity decrease with soil water content, dry soil portions contribute marginally to root water uptake but also limit the contribution of the surrounding (potentially wetter) bulk soil. As long as soil hydraulic conductivities do not limit the water flow to the rhizosphere, root placement and hydraulic properties (i.e. the root hydraulic architecture) have a limited impact on the uptake process, provided that the root system conductance is large enough (Passioura, 1984). The root hydraulic architecture essentially matters in water deficit conditions, when the soil hydraulic conductivity become limiting. Because the array of intermediate situations where the soil is neither completely dry nor wet is large, it has become obvious in the last decade that an appropriate framework to analyze water uptake should consider both root hydraulic architecture and soil hydraulic properties (Draye et al., 2010).

In this Update, we report on recent advances in the analysis of water flow and water uptake regulation within the soil-root domain. In the first three sections, we analyze root and soil features that influence water uptake, with a focus on conditions of limited water supply. In the last two sections, we highlight recent work in systems analysis of root water uptake and review methodological developments that will guide future research in this area.

COINCIDENCE BETWEEN ROOT FORAGING AND SOIL RESOURCES DISTRIBUTION

The importance of root placement for water extraction depends on the ability of the soil to redistribute its water to sustain the uptake of water that occurs in the rhizospheric compartment of the soil. In soils with high water conductivity throughout the season, fast soil water redistribution from the bulk soil to the rhizosphere limits the role of root foraging as long as the root system conductance is large enough. In drying soils, however, the smaller hydraulic conductivity of the soil reduces soil water redistribution and the soil volume from which individual root segments are able to obtain their water narrows down accordingly. In such conditions, even transient, the placement of roots and its correlation to the distribution of soil water sets an upper limit to the amount of water that can be extracted.

In transient or cyclic drought environments, the reserve of soil water can be temporarily restricted to deeper layers because water uptake (and evaporation) occurs preferentially in the topsoil, where the root length density (cumulated root length per unit soil volume) is the highest and the path to extract water the lowest. This situation is most pronounced under terminal drought, as the soil water reserve is not refilled over the growing season and is gradually restricted to deeper soil layers. Increasing the root system depth and tailoring deep water extraction was therefore proposed as a key element of a root system ideotype adapted to water-limited environments (Wasson et al., 2012; Comas et al., 2013; Lynch, 2013). Considering the construction and maintenance costs of root systems, the ideotype should preferably have few and long laterals, evenly distributed along the depth axis (Lynch, 2013). The rationale is that few long laterals have a small weight on the carbon budget and allow the exploration of a larger soil volume. Aerenchyma is also considered as a feature reducing the root construction cost, in favor of deep root extension. Wasson et al. (2012) also advocate for a greater root length density at depth and reduced density in the topsoil to favor deep soil water extraction.

Root system depth appears to be amenable to conventional breeding and has been shown to be under control of, at least, four different quantitative trait loci in rice (Oryza sativa; Courtois et al., 2013) and one major constitutive quantitative trait loci in maize (Zea mays; Landi et al., 2010). In addition, several traits that should contribute to a deep root phenotype have been proposed or identified. Increasing the diameter of the main roots is thought to be linked with a greater growth potential (Pagès et al., 2010) and a greater ability to explore hard soil (Bengough et al., 2011). In rice, the gene DEEPER ROOTING1 has been shown to steepen the root insertion angle and increase the rooting depth, conferring improved drought resistance (Uga et al., 2013). In groundnut (Arachis hypogaea), DEHYDRATION RESPONSE ELEMENT B1A has been shown to increase drought resistance by promoting root development in deep soil layers. Additionally, increasing the proportion of aerenchyma in main root axes reduces the metabolic cost of root exploration (Fan et al., 2007; Lenochova et al., 2009; Zhu et al., 2010). The manipulation of root branching in different layers, which is part of the deep root ideotype, is expected to be more difficult to achieve for practical observation constraints. While considering those traits, it should be reminded that deep rooting could be obtained differently in tap-rooted species compared with monocot root systems with continued production of gravitropic adventitious root axes.

The identification of root ideotypes is further complicated by the fact that root growth and development are strongly influenced by the soil environment. Root architecture remodeling in response to a wide range of nutrient deficiencies has been recently described and partly elucidated in Arabidopsis (Arabidopsis thaliana; Giehl et al., 2013; Gruber et al., 2013). Changes in root architecture in response to phosphate starvation occur under the control of Oryza sativa MYB2 phosphate-responsive gene1 in rice (Dai et al., 2012) and AtSIZ1 in Arabidopsis (Miura et al., 2005, 2011). Interestingly, alternative adaptations to the same adverse conditions exist among different genotypes, as illustrated by altered primary or lateral root growth conferring resistance to K starvation (Kellermeier et al., 2013). Local environmental conditions also contribute to root architecture remodeling. Individual roots are able to reorient toward water (hydrotropism) under the control of MIZU-KUSSEI1 (Iwata et al., 2013) and GNOM (Moriwaki et al., 2014) in Arabidopsis. Similarly, PIN-FORMED2 activity influences the capacity of individual roots to escape high-salinity patches (halotropism; Galvan-Ampudia et al., 2013). This plasticity of root development should not be overlooked in drought resistance studies given the role of water in nutrient uptake.

The benefit of deep root systems in drought-prone environments has been demonstrated experimentally in rice (Steele et al., 2013), wheat (Triticum aestivum; Manschadi et al., 2010), maize (Hammer et al., 2009, 2010), legumes (Vadez et al., 2013), grapes (Vitis vinifera; Alsina et al., 2011), or trees (Pinheiro et al., 2005). However, other results seem to indicate that deep root systems are not always linked to an increase in yield. Experiments with chickpea (Cicer arietinum; Zaman-Allah et al., 2011a, 2011b) and wheat (Schoppach et al., 2013) indicate that drought tolerance, especially in terminal drought conditions, can be linked to a conservative use of water throughout the season rather than deep rooting. In such cases, plants tailored for improved root length density at depth are likely to use too much water early in the season and reduce the reserve of water in the profile during grain filling. A similar behavior has been reproduced using modeling tools (Vadez et al., 2012). As suggested recently, benefits of any root-related trait could be highly dependent on the drought scenario (Genotype × Environment interactions; Tardieu, 2012).

ROOT SYSTEM HYDRAULIC ARCHITECTURE

Although all root segments are somehow connected to the plant stem, the negative water potential that develops at their surface as a result of the xylem tension is not necessarily uniform. Individual root segments are not equally conductive to water, both radially and axially, and the paths that link them to the shoot base are unique (Fig. 1A). On the one side, from the root surface to the xylem vessels, water flows radially, following paths of lowest hydraulic resistance using apoplastic, symplastic, and cell-to-cell pathways. This radial water inflow into the root, described as a composite transport, can be characterized at the root segment level by a radial hydraulic conductance, which has been shown to be variable between species (Bramley et al., 2009; Knipfer et al., 2011) and even ecotypes (Sutka et al., 2011). On the other side, the axial flow along the xylem is characterized by the axial conductance of successive root segments. The complete hydraulic structure of the root system, comprising its topology and the size and hydraulic properties of its constituting segments, forms its root hydraulic architecture (Doussan et al., 1998). Under uniform soil water distribution, it has been shown that the hydraulic architecture allows for predicting the expected contribution of every root segment to the water uptake (Doussan et al., 2006), recently referred to as the standard uptake fractions distribution (Couvreur et al., 2012).

The tissular organization of root segments is a long-term determinant of their radial conductivity (Fig. 2C). This includes the number and anatomy of cell layers between the root surface and the xylem (Yang et al., 2012) and the presence of hydrophobic Casparian strips that occur typically at the endodermis and exodermis (Enstone et al., 2003). The formation of hydrophobic structures has been shown to be influenced by the growing medium (Hachez et al., 2012) and is triggered by drought conditions (Enstone and Peterson, 2005; Vandeleur et al., 2009). As the tissular organization is established permanently, this implies that the radial conductivity reflects the root segment history (its development, in relation with its environment). Beyond these structural features, the root radial conductivity is also controlled on a shorter term by the regulation of water channels, or aquaporins (Cochard et al., 2007b; Hachez et al., 2012) Presence of functional aquaporins in cell membranes highly facilitates the passive flow of water and has been shown to contribute to 20% to 80% of the radial water inflow into the root (Maurel and Chrispeels, 2001; Javot et al., 2003), although this contribution varies between species (Bramley et al., 2009, 2010). Aquaporin regulation is achieved through their expression intensity (Hachez et al., 2012) or subcellular localization (Li et al., 2011) or through the gating of the aquaporin pore (gating; Boursiac et al., 2008). In maize, aquaporins have been shown to be preferentially localized in the endodermis and exodermis (Hachez et al., 2006; Fig. 2C). For more details on aquaporins, see Chaumont and Tyerman (2014).

Figure 2.

Water flow in the plant. A, Water flow in the plant is a passive process driven by water potential differences and regulated by hydraulic conductivities between the compartments of the system (soil-root-shoot-atmosphere). B, Axial water flow is influenced by the anatomy of the xylem pipes (size, number, and presence of pits) and the occurrence of cavitation events (embolism of xylem elements). C, Radial water flow is influenced, in the long term, by the radial anatomy of the root, such as the number of cell layers and the presence of hydrophobic layers (endodermis and exodermis). In the short term, the radial flow is influenced by the expression and localization of aquaporins.

Figure 2.

Water flow in the plant. A, Water flow in the plant is a passive process driven by water potential differences and regulated by hydraulic conductivities between the compartments of the system (soil-root-shoot-atmosphere). B, Axial water flow is influenced by the anatomy of the xylem pipes (size, number, and presence of pits) and the occurrence of cavitation events (embolism of xylem elements). C, Radial water flow is influenced, in the long term, by the radial anatomy of the root, such as the number of cell layers and the presence of hydrophobic layers (endodermis and exodermis). In the short term, the radial flow is influenced by the expression and localization of aquaporins.

As for the radial conductance, both permanent and transient features affect the axial conductance of individual root segments. Structural features include the number, size, degree of interconnection, and decorations of xylem vessels (Vercambre et al., 2002; Domec et al., 2006; Tombesi et al., 2010; Fig. 2B). The number and size of xylem vessels increase during the maturation of root segments and decrease with branching order in cereals (Watt et al., 2008). The xylem diameter reflects the root segment history. For example, it tends to be lower in shallow roots than in deep roots for woody plants growing in environments subject to drought or freezing conditions (Gebauer and Volařík, 2012). The anatomy of xylem vessels also displays a large variability in Zea spp. (Burton et al., 2013), rice (Uga et al., 2008), legumes (Purushothaman et al., 2013), or coniferous (McCulloh et al., 2010). Transient modifications of the axial conductance occur as a result of xylem vessel embolism, or cavitation, following the nucleation and rapid expansion of gas bubbles under high tension. Because embolized vessels are not hydraulically conductive, the flow of water through the root segment is restricted to the remaining, noncavitated vessels. Different species are not equally susceptible to cavitation (Cochard et al., 2008) or even cultivars (Cochard et al., 2007a; Li et al., 2009; Rewald et al., 2011), but not always (Lamy et al., 2014). Susceptibility to cavitation has been linked to the large xylem vessels, anatomy of walls, and pits (Delzon et al., 2010; Herbette and Cochard, 2010; Christman et al., 2012). It has to be noted that xylem vessel cavitation is a reversible event, although the exact mechanisms underlying the refilling processes are not yet fully known (Zwieniecki and Holbrook, 2009). It is often considered that the axial conductance does not limit water flow in the root system by virtue of the large conductivity of xylem vessels (Steudle, 2000). However, recent experimental evidence has revealed the negative effect of cavitation on the plant water status (Zufferey et al., 2011; Johnson et al., 2012).

Novel root hydraulic architectures are being proposed to improve drought tolerance. Wasson et al. (2012) advocate for greater axial and radial conductivities in deep roots to increase the uptake and transport capacity of water from deep soil layers. In conditions of scarce deep water, Comas et al. (2013) recommend decreasing the axial conductance to save water for the end of the crop cycle. More generally, the importance of the ratio between axial and radial conductivities has also been stressed from modeling studies (Doussan et al., 2006; Draye et al., 2010). Large values of this ratio should lead toward a uniform distribution of the uptake throughout the entire root system, while low values would favor preferential uptake in the topsoil. Experimental evidence that the manipulation of root hydraulic architecture can improve the water status of plants under water deficit remains scanty (Passioura, 2012). Designing a root hydraulic architecture to improve drought tolerance is thus likely to be specific to the species and genotype, climatic scenario, soil hydraulic properties, and management practices (Draye et al., 2010).

INFLUENCE OF THE SOIL WATER DISTRIBUTION

The above statement that the distribution of water uptake among root segments should be predictable from the sole root hydraulic architecture is only valid under conditions of uniform soil water potential that are generally encountered in well-watered soils (Doussan et al., 1998). Under heterogeneous conditions, at places where the soil water potential is low, soil capillary forces retain water more strongly in the remaining fraction of the soil porosity, comprised of small micropores. As this reduces the soil hydraulic conductivity, the flow of water toward the root surface is locally restricted, and water uptake by other root segments, located in portions of the soil where water is more readily available, should increase to maintain the global transpiration stream. This passive adjustment of the distribution of water uptake among root segments occurring as a consequence of the heterogeneity of soil water potential (Fig. 3) and conductivity was called compensatory root water uptake (Jarvis, 1976; Šimůnek and Hopmans, 2009). When compensation occurs, the root distribution becomes a very poor indicator of the distribution of the uptake sites, as root length density and uptake profiles become dissimilar (Javaux et al., 2013). Couvreur et al. (2012) recently highlighted that the compensatory uptake can be formulated as the product of three terms, the standard uptake fraction (see above), the difference between the local and spatially averaged soil water potential, and the root system conductance, which suggests that, in addition to defining the standard sites of water uptake, the root hydraulic architecture simultaneously contributes to the adjustment of the uptake to the soil water potential distribution and influences soil water potential heterogeneity. Interestingly, simulation studies indicate that compensatory root water uptake precedes the moment where transpiration is affected (Couvreur et al., 2012). All these results converge to a contribution of compensatory root water uptake to the maintenance of transpiration and assimilation.

Figure 3.

Influence of the soil water potential distribution on the water uptake process. The model (Javaux et al., 2008) was used to simulate the root radial water flow under different soil water potential distribution. A, Radial water flow (top) under hydrostatic equilibrium (bottom). B, Compensatory root water uptake (top) for different soil water potential distributions (bottom). Relative units compared with A.

Figure 3.

Influence of the soil water potential distribution on the water uptake process. The model (Javaux et al., 2008) was used to simulate the root radial water flow under different soil water potential distribution. A, Radial water flow (top) under hydrostatic equilibrium (bottom). B, Compensatory root water uptake (top) for different soil water potential distributions (bottom). Relative units compared with A.

A particular scenario of soil water redistribution involving the root hydraulic architecture can occur under low or negligible transpiration flow. In such conditions, the xylem water potential is a weighted value of the soil water potentials sensed by root segments, intermediate between the soil water potential of the driest and wettest soil parts in contact with roots. As long as root segments are radially conductive to water, the root system offers a long-distance path of low hydraulic resistance that allows the hydraulic lift phenomenon, whereby soil water is redistributed through the root system from the wetter soil regions toward the drier ones. This phenomenon, which has long been a matter of debate, would contribute to the night restoration of the soil hydraulic conductivity that decreased around part of the root system as a result of root water uptake during the day (McMichael and Lascano, 2010).

Other factors that reduce the soil hydraulic conductivity have been recently demonstrated. Following the mass conservation principle, the flux density of water (motion speed) increases as it gets closer to the root surface, and, in parallel, its water potential decreases as well as the soil conductivity. The rhizosphere is thus susceptible to a local drop of hydraulic conductivity that is favored by high rates of root water uptake and by soil properties, such as coarse textures, that steepen the relationship between soil conductivity and water potential (Shroeder et al., 2008). Soil hydraulic properties and water potential around each root segment therefore set a maximum uptake rate above which a soil restriction to water flow is likely to occur. Interestingly, this phenomenon would be difficult to distinguish from the limitation imposed by root hydraulic properties that is observed under drought (Schoppach et al., 2013).

The specific hydraulic properties of the rhizosphere have been reviewed recently (Carminati and Vetterlein, 2013). Strikingly, its complex constitution seems to generate hydrophilic or hydrophobic behaviors depending on the environmental conditions (Carminati et al., 2011; Moradi et al., 2012). The role of this plasticity is not yet fully understood but is proposed to participate in the control of the soil conductivity by the roots themselves, which would add a level of complexity in our model of the regulation of water uptake.

MODELING CAN HELP EXPLAIN THE DYNAMICS OF ROOT WATER UPTAKE

Despite the fact that water uptake follows simple rules of passive flow driven by water potential gradients and following paths of lowest resistance, and despite our knowledge of the main paths and factors affecting their conductivities, our understanding of water uptake at the plant and seasonal scale remains limited by the difficulties in integrating those interacting paths and factors, at the appropriate scales and in a spatial and temporal framework. Many of those factors have been evoked in the above sections, but many others have been deliberately set aside, such as the feedback effect of water uptake on root growth via its effects on, for example, assimilation and soil mechanical impedance. Because direct experimental observations are necessarily capturing limited aspects of water uptake, systems approaches gained much interest in the last decade (Dunbabin et al., 2013; Hill et al., 2013).

Doussan et al. (2006) presented the first model that simulates water flows explicitly in the soil-root continuum. Using the concept of hydraulic architecture to solve plant water flow (Doussan et al., 1998) and Richards equations to solve water flow in unsaturated soils, this model was able to simulate compensatory uptake and hydraulic lift in heterogeneous soil conditions. A very similar approach was taken by Javaux et al. (2008) to implement the soil-root hydraulic model R-SWMS. Using the model R-SWMS, Schroeder et al. (2009) illustrated the negative impact of local conductivity drops around roots in drying soils on the water uptake process. The importance of the ratio between axial and radial root conductivities and of the soil type was also highlighted (Draye et al., 2010). On the soil side, the model can be instrumental to investigate the influence of the root water uptake on water flow and nutrient transport in the surrounding soil (Schroeder et al., 2012). Recently, it was used to assess the impact of salinity on the plant transpiration reduction (Schroeder et al., 2013). To streamline the adoption of these tools by the plant science community, Couvreur et al. (2012) proposed a simplified version of R-SWMS that can be used at the crop level but still relies on a precise parameterization of root hydraulic architecture. This simplified model has also been shown to simulate behaviors such as compensatory uptake and hydraulic lift from hydraulic principles (Javaux et al., 2013).

METHODS TO INVESTIGATE ROOT WATER UPTAKE DYNAMICS

The development of measurement techniques and observation methods has been instrumental in many recent advance of our understanding of root water uptake dynamics. While traditional methods to investigate either plant or soil properties are mainly used at the plant scale, new techniques have empowered a more detailed approach of the system, down to the centimeter scale.

Several two- and three-dimensional observation methods have been developed that enable better or faster characterization of root system architecture. Pouches dipping in nutrient solution are becoming increasingly popular to screen early stages of root systems development in two dimensions (Hund et al., 2009). Recently, a scanning technique has been proposed for digitizing entire root systems of plants grown in rhizoboxes (Lobet and Draye, 2013). The two-dimensional restriction of pouches and rhizotrons was recently released by stereo imaging of root systems grown in tubes filled with gellan gum (Iyer-Pascuzzi et al., 2010; Clark et al., 2011). Lastly, x-ray computed tomography (Mooney et al., 2012) or magnetic resonance imaging (Jahnke et al., 2009), widely used in medical sciences, is now entering the plant research domain. These allow the three-dimensional noninvasive monitoring of root growth in realistic soil cores and, in the future, should provide many details on the precise soil conditions around individual root segments, including soil water content.

Following the development of these observation techniques, specific free software solutions were developed for the analysis of root system architecture and root anatomy. For example, RootNav (Pound et al., 2013), SmartRoot (Lobet et al., 2011), RootReader2D (Clark et al., 2013), EZ-Rhizo (Armengaud et al., 2009), and Root System Analyzer (Leitner et al., 2014) were developed for the analysis of two-dimensional root images, while RooTrak (Mairhofer et al., 2013) and RootReader3D (Clark et al., 2011) were designed for the analysis of stereo images. These tools ease the digitizing and analysis of complex root system architecture. At the organ scale, RootScan (Burton et al., 2012) was developed for the high-throughput analysis of the anatomy of root sections. The software automatically computes the area of multiple root tissues including the aerenchyma or the xylem vessels. These tools have been recently included on the Plant Image Analysis database (http://www.plant-image-analysis.org; Lobet et al., 2013).

The quantification of root hydraulic properties remains certainly one of the biggest challenges. Techniques suitable for global measurements have been established for many years. The pressure chamber is widely used and estimates the conductance from the measurement of the water flow induced by a known pressure differential. Other techniques estimate the conductance of individual root segments, yet remain extremely time consuming (e.g. pressure clamp [Bramley et al., 2007] and pressure probe [Steudle and Peterson, 1998]). Part of the challenge lies in the plasticity of root hydraulic properties as a function of segment type and age and environmental conditions and in the variability between measurement methods (Bramley et al., 2007).

On the opposite, an array of techniques is available to monitor soil water content in one, two, and even three dimensions. This includes time domain reflectometry (Robinson et al., 2003; Walker et al., 2004), electrical resistance tomography (Vanderborght et al., 2005; Cassiani et al., 2006; Beff et al., 2013), or, more recently, ground-penetrating radar (Lambot et al., 2008). The spatial resolution of these techniques ranges in the decimeter scale and is appropriate to study the distribution of water in rows or interrows. Recently, two techniques have been successfully tested for the observation of water flow down to the centimeter level. Light transmission imaging can be used to finely map changes in soil water content in transparent rhizotrons (Garrigues et al., 2006). Unfortunately, the technique is restricted to a specific type of substrate (white sand) and does not estimate water uptake by individual roots due to the unknown redistribution of the water in the substrate (Javaux et al., 2008). More recently, the use of neutron radiography (Esser et al., 2010) that is not bound to any specific type of substrate has been used to investigate water movement and determine water uptake sites in lupin (Lupinus albus) root systems. Using D2O injection in combination with a convection-diffusion model, water uptake by individual segments could be quantified in a complete root system (Zarebanadkouki et al., 2013). This technical evolution is therefore promising new insights on the water dynamics at smaller scales, while systems analysis frameworks will help to integrate this information.

CONCLUSION

The determinants of water flow through the soil-root system are well known and have been largely studied individually. However, their integration at the plant and canopy scales and over a whole crop cycle remains very limited. The spatial and temporal heterogeneity of the soil, the interactions between the soil and the root at multiple scales, and the need to combine very different disciplines makes this integration particularly difficult. With the development of functional-structural soil-plant models, root systems biology is bringing novel analytical tools to turn a vast amount of data into biological questions crossing scales and disciplines. We believe that new root system ideotypes could emerge from a more comprehensive and quantitative consideration of the many determinants of water flow during a whole crop cycle and in the framework of a cost-benefit analysis at the system level.

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