Focus Issue on Roots: Root Cortical Aerenchyma Enhances Nitrogen Acquisition from Low-Nitrogen Soils in Maize

Suboptimal nitrogen (N) availability is a primary constraint for crop production in developing nations, while in rich nations, intensive N fertilization carries substantial environmental and economic costs. Therefore, understanding root phenes that enhance N acquisition is of considerable importance. Structural-functional modeling predicts that root cortical aerenchyma (RCA) could improve N acquisition in maize (Zea mays). We evaluated the utility of RCA for N acquisition by physiological comparison of maize recombinant inbred lines contrasting in RCA grown under suboptimal and adequate N availability in greenhouse mesocosms and in the field in the United States and South Africa. N stress increased RCA formation by 200% in mesocosms and by 90% to 100% in the field. RCA formation substantially reduced root respiration and root N content. Under low-N conditions, RCA formation increased rooting depth by 15% to 31%, increased leaf N content by 28% to 81%, increased leaf chlorophyll content by 22%, increased leaf CO₂ assimilation by 22%, increased vegetative biomass by 31% to 66%, and increased grain yield by 58%. Our results are consistent with the hypothesis that RCA improves plant growth under N-limiting conditions by decreasing root metabolic costs, thereby enhancing soil exploration and N acquisition in deep soil strata. Although potential fitness tradeoffs of RCA formation are poorly understood, increased RCA formation appears be a promising breeding target for enhancing crop N acquisition.Nitrogen (N) deficiency is one of the most limiting factors in maize (Zea mays) production worldwide (Ladha et al., 2005). In developing countries such as those in sub-Saharan Africa, less than 20 kg N ha⁻¹ is applied to fields of smallholder farmers due to high fertilizer cost (Azeez et al., 2006; Worku et al., 2007). In developed countries, intensive N fertilization is used to maintain satisfactory yield (Tilman et al., 2002). In the United States, N fertilizers are the greatest economic and energy cost for maize production (Ribaudo et al., 2011). However, less than half of the N applied to crops is actually acquired, and most of the remaining N becomes a source of environmental pollution (Raun and Johnson, 1999; Smil, 1999; Tilman et al., 2002). For example, N and phosphorus (P) effluents into marine systems from agriculture cause eutrophication and hypoxic zones (Diaz and Rosenberg, 2008; Robertson and Vitousek, 2009). Nitrate contamination in surface water and groundwater systems poses serious health risks, such as methemoglobinemia and N-nitroso-induced cancers (UNEP and WHRC, 2007). Emission of nitrous oxides from agricultural activities contributes to ozone damage and global warming (Kulkarni et al., 2008; Sutton et al., 2011). Furthermore, the production of N fertilizers requires considerable energy from fossil fuels, and since energy costs have risen in recent years, farmers face economic pressure from increasing N fertilizer costs, which are linked to higher food prices. It is estimated that a 1% increase in crop N efficiency could save more than $1 billion (U.S.) annually worldwide (Kant et al., 2011). Therefore, even a small improvement in N efficiency would have significant positive impacts on the environment and the economy.Soil N is heterogenous and dynamic. The bioavailability of soil N depends on the balance between the rates of mineralization, nitrification, and denitrification. These processes are determined by several factors, including soil composition, microbial activity, soil temperature, and soil water status (Miller and Cramer, 2004). The predominant form of soil N available to plants in most agricultural systems is nitrate, which is highly soluble in water and thus mobile in the soil (Barber, 1995; Marschner, 1995). Mineralization of organic matter and/or the application of N fertilizer at the beginning of the growing season followed by precipitation and irrigation create a pulse of nitrate that may exceed the N acquisition capacity of seedlings and leach below the root zone. Therefore, it has been proposed that increasing the speed of root exploration of deep soil strata could benefit N acquisition (Lynch, 2013). However, the structural investments and metabolic expenditures of root systems are substantial and can exceed half of daily photosynthesis (Lambers et al., 2002). Therefore, full consideration of the costs and benefits of root systems is crucial for identifying root traits to improve crop production, especially in water- and nutrient-deficient environments (Lynch, 2007). Taking rhizoeconomics and the spatiotemporal availability of soil N into account, Lynch (2013) proposed a root ideotype for enhanced N acquisition in maize called Steep, Cheap, and Deep, in which Steep refers to architectural phenes and Cheap refers to phenes that reduce the metabolic cost of soil exploration. One element of this ideotype is abundant root cortical aerenchyma (RCA).RCA consists of enlarged air spaces in the root cortex (Esau, 1977). RCA is known to form in response to hypoxia, and the role of RCA in improving oxygen transport to roots of many plant species under hypoxic conditions has been well researched (Vartapetian and Jackson, 1997; Jackson and Armstrong, 1999; Mano and Omori, 2007, 2013). Interestingly, RCA can also form in response to drought and edaphic stresses such as N, P, and sulfur deficiencies (Drew et al., 1989; Bouranis et al., 2003; Fan et al., 2003; Zhu et al., 2010a), which suggests that the benefit of RCA extends beyond facilitating oxygen transport. Several lines of evidence suggest that RCA enhances root metabolic efficiency under stress. Fan et al. (2003) found that RCA formation significantly reduced root segment respiration and P content of root tissue, which allowed greater shoot growth in soils with low P availability. Under drought, maize genotypes with high RCA formation had greater root length, deeper rooting, better leaf water status, and 8 times greater yield than closely related genotypes with low RCA (Zhu et al., 2010a). Effects of RCA on root respiration were more pronounced for large-diameter roots compared with small-diameter roots (Jaramillo et al., 2013). Results from the functional-structural plant model SimRoot showed that RCA formation could be an adaptive response to deficiency of N, P, and potassium by decreasing the metabolic cost of soil exploration. By reducing root respiration, RCA decreases the carbon cost of soil exploration, and by decreasing the N and P content of root tissue, RCA permits internal reallocation of nutrients to growing root tissue, which is particularly beneficial under conditions of low N and P availability (Postma and Lynch, 2011a). Under suboptimal P availability, RCA increased the growth of a simulated 40-d-old maize plant by 70% (Postma and Lynch, 2011b). In the case of N, RCA increased the growth of simulated maize plants up to 55% in low-N conditions, and plants benefit from RCA more in high-N-leaching environments than in low-N-leaching environments (Postma and Lynch, 2011a). In addition, the formation of RCA decreases critical soil nutrient levels, defined as the soil fertility below which growth is reduced, suggesting that cultivars with high RCA may require less fertilizer under nonstressed conditions. These in silico results suggest that RCA has potential utility for improving crop nutrient acquisition in both high- and low-input agroecosystems.The overall objective of this research was to assess the utility of RCA for N acquisition in maize under N-limiting conditions. Maize near-isophenic recombinant inbred lines (RILs) sharing a common genetic background (i.e. descending from the same parents) with common root phenotypes but contrasting in RCA formation were grown under N stress to test the hypothesis that RCA formation is associated with reduced root respiration, reduced tissue nutrient content, greater rooting depth, enhanced N acquisition, and therefore greater plant growth and yield under N limitation.     

Focus Issue on Roots: Low Crown Root Number Enhances Nitrogen Acquisition from Low-Nitrogen Soils in Maize

In developing nations, low soil nitrogen (N) availability is a primary limitation to crop production and food security, while in rich nations, intensive N fertilization is a primary economic, energy, and environmental cost to crop production. It has been proposed that genetic variation for root architectural and anatomical traits enhancing the exploitation of deep soil strata could be deployed to develop crops with greater N acquisition. Here, we provide evidence that maize (Zea mays) genotypes with few crown roots (crown root number [CN]) have greater N acquisition from low-N soils. Maize genotypes differed in their CN response to N limitation in greenhouse mesocosms and in the field. Low-CN genotypes had 45% greater rooting depth in low-N soils than high-CN genotypes. Deep injection of ¹⁵N-labeled nitrate showed that low-CN genotypes under low-N conditions acquired more N from deep soil strata than high-CN genotypes, resulting in greater photosynthesis and plant N content. Under low N, low-CN genotypes had greater biomass than high-CN genotypes at flowering (85% in the field study in the United States and 25% in South Africa). In the field in the United States, 1.8× variation in CN was associated with 1.8× variation in yield reduction by N limitation. Our results indicate that CN deserves consideration as a potential trait for genetic improvement of N acquisition from low-N soils.Maize (Zea mays) is one of the world’s most important crops and is a staple food in Latin America and Africa. Maize production requires a large amount of fertilizer, especially nitrogen (N). In the United States, N fertilizers represent the greatest economic and energy costs for maize production (Ribaudo et al., 2011). However, on-farm studies across the northcentral United States revealed that more than half of applied N is not taken up by maize plants and is vulnerable to losses from volatilization, denitrification, and leaching, which pollute air and water resources (Cassman et al., 2002). Conversely, in developing countries, suboptimal N availability is a primary limitation to crop yields and, therefore, food security (Azeez et al., 2006). Increasing yield in these areas is an urgent concern, since chemical fertilizers are not affordable (Worku et al., 2007). Cultivars with greater N acquisition from low-N soils could help alleviate food insecurity in poor nations as well as reduce environmental degradation from excessive fertilizer use in developed countries.The two major soil N forms available to plants are ammonium and nitrate. Nitrate is the main N form in most maize production environments (Miller and Cramer, 2004). Nitrate is highly mobile in soil, and the spatiotemporal availability of soil N is rather complex. In the simplest case, N fertilizers applied to the soil and/or N released from the mineralization of soil organic matter are rapidly converted to nitrate by soil microbes. After irrigation and precipitation events, nitrate moves with water to deeper soil strata. Leaching of nitrate from the root zone has been shown to be a significant cause of low recovery of N fertilizer in commercial agricultural systems (Raun and Johnson, 1999; Cassman et al., 2002). Differences in root depth influence the ability of plants to acquire N. Studies using ¹⁵N-labeled nitrate placed at different soil depths showed that only plants with deep rooting can acquire N sources from deep soil strata, which would otherwise have been lost through leaching (Kristensen and Thorup-Kristensen, 2004a, 2004b). Therefore, selection for root traits enhancing rapid deep soil exploration could be used as a strategy to improve crop N efficiency.The maize root system consists of embryonic and postembryonic components. The embryonic root system consists of two distinct root classes: a primary root and a variable number of seminal roots formed at the scutellar node. The postembryonic root system consists of roots that are formed at consecutive shoot nodes and lateral roots, which are initiated in the pericycle of all root classes. Shoot-borne or nodal roots that are formed belowground are called crown roots, whereas those that are formed aboveground are designated brace roots (Hochholdinger, 2009). While the primary root and seminal roots are essential for the establishment of seedlings after germination, nodal roots and particularly crown roots make up most of the maize root system and are primarily responsible for soil resource acquisition later in development (Hoppe et al., 1986).Lynch (2013) proposed an ideotype for superior N and water acquisition in maize called Steep, Cheap, and Deep (SCD), which integrates root architectural, anatomical, and physiological traits to increase rooting depth and, therefore, the capture of N in leaching environments. One such trait is crown root number (CN). CN is an aggregate trait consisting of the number of belowground nodal whorls and the number of roots per whorl. The crown root system dominates resource acquisition during vegetative growth after the first few weeks and remains important during reproductive development (Hochholdinger et al., 2004). CN in maize ranges from five to 50 under fertile conditions (Trachsel et al., 2011). At the low end of this range, crown roots may be too spatially dispersed to sufficiently explore the soil. There is also a risk of root loss to herbivores and pathogens. If roots are lost in low-N plants, there may be too few crown roots left to support the nutrient, water, and anchorage needs of the plant. At the high end, a large number of crown roots may compete with each other for water and nutrients as well as incur considerable metabolic costs for the plant (Fig. 1). The SCD ideotype proposes that there is an optimal CN for N capture in maize (Lynch, 2013). Under low-N conditions, resources for root growth and maintenance are limiting, and nitrate is a mobile resource that can be captured by a dispersed root system. The optimal CN should tend toward the low end of the phenotypic variation to make resources available for the development of longer, deeper roots rather than more crown roots. According to the SCD ideotype, in low-N soils, maize genotypes with fewer crown roots could explore soils at greater depth, resulting in greater N acquisition, growth, and yield than genotypes with many crown roots.Open in a separate windowFigure 1.Visualization of the maize root system of low- and high-CN genotypes at 40 d after germination. Crown roots are colored in blue, and seminal roots are in red. The CN is eight in the low-CN genotype and 46 in the high-CN genotype. (Image courtesy of Larry M. York.)The objective of this study was to test the hypotheses that (1) low-CN genotypes have greater rooting depth than high-CN genotypes in low-N soils; (2) low-CN genotypes are better at acquiring deep soil N than high-CN genotypes; and (3) low-CN genotypes have greater biomass and yield than high-CN genotypes in low-N conditions.     

Focus Issue on Roots: Image-Based High-Throughput Field Phenotyping of Crop Roots

Current plant phenotyping technologies to characterize agriculturally relevant traits have been primarily developed for use in laboratory and/or greenhouse conditions. In the case of root architectural traits, this limits phenotyping efforts, largely, to young plants grown in specialized containers and growth media. Hence, novel approaches are required to characterize mature root systems of older plants grown under actual soil conditions in the field. Imaging methods able to address the challenges associated with characterizing mature root systems are rare due, in part, to the greater complexity of mature root systems, including the larger size, overlap, and diversity of root components. Our imaging solution combines a field-imaging protocol and algorithmic approach to analyze mature root systems grown in the field. Via two case studies, we demonstrate how image analysis can be utilized to estimate localized root traits that reliably capture heritable architectural diversity as well as environmentally induced architectural variation of both monocot and dicot plants. In the first study, we show that our algorithms and traits (including 13 novel traits inaccessible to manual estimation) can differentiate nine maize (Zea mays) genotypes 8 weeks after planting. The second study focuses on a diversity panel of 188 cowpea (Vigna unguiculata) genotypes to identify which traits are sufficient to differentiate genotypes even when comparing plants whose harvesting date differs up to 14 d. Overall, we find that automatically derived traits can increase both the speed and reproducibility of the trait estimation pipeline under field conditions.Crop root systems represent an underexplored target for improvements as part of community efforts to ensure that global crop yields and productivity keep pace with population growth (Godfray et al., 2010; Gregory and George, 2011; Nelson et al., 2012). The challenge in improving crop root systems is that yield and productivity also depend on soil fertility, which is also a major constraint to global food production (Lynch, 2007). Hence, desired improvements to crop root systems include enhanced water use efficiency and water acquisition given the increased likelihood of drought in future climates (Intergovernmental Panel on Climate Change, 2014). Over the long term, the development of crop genotypes with improved root phenotypes requires advances in the characterization of root system architecture (RSA) and in the relationship between RSA and function.The emerging discipline of plant phenomics aims to expand the scope, throughput, and accuracy of plant trait estimates (Furbank, 2009). In the case of plant roots, structural traits may describe RSA as geometric or topological measures of the root shape at various scales (e.g. diameters and width of the whole root system or a single branch; Lynch, 1995; Den Herder et al., 2010). These traits can be used to predict yield under specific conditions such as drought or low fertility. Understanding the diversity and development of root architectural traits is crucial, because spatial and temporal root deployment affects plant fitness, especially water and nutrient acquisition (Rich and Watt, 2013). Thus, improving plant performance may benefit from improvements in the characterization of root architecture, including understanding how trait variation arises as a function of genotype and environmental conditions (Band et al., 2012; Shi et al., 2013).Current efforts to understand the structure of crop root systems have already led to a number of imaging solutions (Lobet et al., 2013) that are able to extract root architecture traits under various conditions (Fiorani et al., 2012), including laboratory conditions (de Dorlodot et al., 2007) in which plants are often grown in pots or glass containers (Zeng et al., 2008; Armengaud et al., 2009; Le Bot et al., 2010; Clark et al., 2011; Lobet et al., 2011; Naeem et al., 2011; Galkovskyi et al., 2012). In the case of pots, expensive magnetic resonance imaging technologies represent one noninvasive approach to capture high-resolution details of root architecture (Schulz et al., 2013), similar to the capabilities of x-ray microcomputed tomography (μCT) systems. X-ray systems allow capturing of the root architecture at a fine scale in containers with a wide variety of soil types (Mairhofer et al., 2012; Mooney et al., 2012). It has been shown that x-ray μCT paired with specifically designed algorithms has sufficient resolution to recover the root structure in many cases (Mairhofer et al., 2013). Nevertheless, x-ray μCT systems are currently unable to image mature root systems because of technical restrictions in container size.As an alternative, root systems can be imaged directly with a digital camera when grown in glass containers with transparent media such as gellan gum or transparent soil replacements (Downie et al., 2012). Such in situ imaging benefits from controlled lightning conditions during image acquisition, even more so when focusing on less complex root structures of younger plants that allow three-dimensional reconstruction (Clark et al., 2011; Topp et al., 2013). Under such controlled conditions, it is expected that imaging would enable the study of growth of roots over time (Spalding and Miller, 2013; Sozzani et al., 2014).However, all of the above-listed solutions have been used primarily to assess root structures in the early seedling stage (French et al., 2009; Brooks et al., 2010; Sozzani et al., 2014) to approximately 10 d after germination (Clark et al., 2011), which makes it all but impossible to directly observe mature root systems. For example, primary and seminal roots make up the major portion of the seedling root system in maize (Zea mays) during the first weeks after germination. Later in development, postembryonic shoot-borne roots become the major component of the maize root system (Hochholdinger, 2009), not yet accessible to laboratory phenotyping platforms. In addition to phenological limitations, current phenotyping approaches for root architecture require specialized growth conditions with aerial and soil environments that differ from field conditions; the effects of such differences on RSA are only sparsely reported in literature (Hargreaves et al., 2009; Wojciechowski et al., 2009).Indeed, high-throughput field phenotyping can be seen as a new frontier for crop improvement (Araus and Cairns, 2014) because imaging a mature root system under realistic field conditions poses unique challenges and opportunities (Gregory et al., 2009; Zhu et al., 2011; Pieruschka and Poorter, 2012). Challenges are intrinsic to roots grown in the field because the in situ belowground imaging systems to date are unable to capture fine root systems. As a consequence, initial attempts to characterize root systems in the field focused on the manual extraction of structural properties. Manual approaches analyzed the root system’s branching hierarchy in relation to root length and rooting depth (Fitter, 1991). In the late 1980s, imaging techniques were first used (Tatsumi et al., 1989) to estimate the space-filling behavior of roots, an estimation process that was recently automated (Zhong et al., 2009). A weakness of such approaches is that exact space-filling properties, such as the fractal dimension, are sensitive to the incompleteness of the excavated root network (Nielsen et al., 1997, 1999). In particular, the box counting method was criticized for above-ground branching networks of tree crowns (Da Silva et al., 2006). The same critiques apply to root systems, because fine secondary or tertiary roots can be lost or cutoff or can adhere to each other during the cleaning process, making it impossible to analyze the entire network.As an alternative, the shovelomics field protocol has been proposed to characterize the root architecture of maize under field conditions (Trachsel et al., 2011). In shovelomics, the researcher excavates the root at a radius of 20 cm around the hypocotyl and 20 cm below the soil surface. This standardized process captures the majority of the root system biomass within the excavation area. After excavation, the shoot is separated from the root 20 cm above the soil level and washed in water containing mild detergent to remove soil. The current procedure places the washed root on a phenotyping board consisting of a large protractor to measure dominant root angles with the soil level at depth intervals and marks to score length and density classes of lateral roots. A digital caliper is used to measure root stem diameters (Fig. 1). Observed traits vary slightly from crop to crop but generally fit into the following categories by depth or root class: angle, number, density, and diameter. In this way, field-based shovelomics allows the researcher to visually quantify the excavated structure of the root crown and compare genotypes via a common set of traits that do not depend on knowledge or observation of the entire root system network. Of note, shovelomics is of particular use in developing countries, which have limited access to molecular breeding platforms (Delannay et al., 2012), and for which direct phenotypic selection is an attractive option.Open in a separate windowFigure 1.A, Classic shovelomics scoring board to score the angle of maize roots with the soil tissue. B, An example to score rooting depth and angle in common bean.Nonetheless, direct phenotypic selection for root traits of field-grown root systems comes with a number of caveats and drawbacks. To date, the quantification of mature root systems is highly dependent on the researcher, reducing the repeatability of measured quantities (Gil et al., 2007). In addition, manual approaches impose limitations on both the number of accessible traits and the number of samples collected. For example, a typical shovelomics phenotyper can gather 10 to 12 traits from a sample in 2 min or approximately 200 samples in a typical workday (Trachsel et al., 2011). This means that evaluating a statistically more significant trial of 1,000 samples may take 5 d or more. Such a time span introduces variation owing to plant growth and phenology. On the other hand, shovelomics is adaptable to both monocot and dicot roots; therefore, its procedure can be scaled over the huge variety of root morphologies in dicots and monocots. For these reasons, we contend that shovelomics represents a valued target for an automated high-throughput phenotyping approach in the field.We introduce an imaging approach for high-throughput phenotyping of mature root systems under realistic soil conditions to address the limitations of manual data collection (Fig. 2, I and II). Our new algorithms directly extract root architecture traits from excavated root images (Fig. 2, III and IV). Rapid imaging of the roots and decoupled analysis of the collected data at a later time improve both the throughput and synchronization of experiments. In addition to traits that are developed from shovelomics, our algorithms extract previously inaccessible traits such as root tip diameter, spatial distribution, and root tissue angle (RTA). Overall, our approach allows imaging directly at field sites with an easily reproducible imaging setup and involves no transportation cost or expensive hardware. We demonstrate the utility of our approach in the field-based analysis of maize and cowpea (Vigna unguiculata) architecture.Open in a separate windowFigure 2.I, Imaging board on the example of a maize root. The experiment tag is used to capture an experiment number, and the scale marker allows the correction of camera tilting and transforming image coordinates into metric units. II, Camera mounted on a tripod placed on top of the imaging board coated with blackboard paint. Note that images were taken with protection against direct sunlight not shown in the image. III, Example of the segmentation of the original image into a binary image and then into a series of image masks that serve as input to estimate traits for monocot and dicot roots. The sample is that of a maize root, 40 d after planting at the URBC. IV, The imaging pipeline for dicot roots and sparse monocot roots: Original image on the imaging board (a), derived distance map where the lighter gray level represents a larger diameter of the imaged object (b), medial axis includes loops (c), and loop RTP with a sample of the root branching structure (d). Colors are randomly assigned to each path. The sample is that of a cowpea root, approximately 30 d after planting at the URBC.     

Focus Issue on Roots: The Optimal Lateral Root Branching Density for Maize Depends on Nitrogen and Phosphorus Availability

Observed phenotypic variation in the lateral root branching density (LRBD) in maize (Zea mays) is large (1–41 cm⁻¹ major axis [i.e. brace, crown, seminal, and primary roots]), suggesting that LRBD has varying utility and tradeoffs in specific environments. Using the functional-structural plant model SimRoot, we simulated the three-dimensional development of maize root architectures with varying LRBD and quantified nitrate and phosphorus uptake, root competition, and whole-plant carbon balances in soils varying in the availability of these nutrients. Sparsely spaced (less than 7 branches cm⁻¹), long laterals were optimal for nitrate acquisition, while densely spaced (more than 9 branches cm⁻¹), short laterals were optimal for phosphorus acquisition. The nitrate results are mostly explained by the strong competition between lateral roots for nitrate, which causes increasing LRBD to decrease the uptake per unit root length, while the carbon budgets of the plant do not permit greater total root length (i.e. individual roots in the high-LRBD plants stay shorter). Competition and carbon limitations for growth play less of a role for phosphorus uptake, and consequently increasing LRBD results in greater root length and uptake. We conclude that the optimal LRBD depends on the relative availability of nitrate (a mobile soil resource) and phosphorus (an immobile soil resource) and is greater in environments with greater carbon fixation. The median LRBD reported in several field screens was 6 branches cm⁻¹, suggesting that most genotypes have an LRBD that balances the acquisition of both nutrients. LRBD merits additional investigation as a potential breeding target for greater nutrient acquisition.At least four major classes of plant roots can be distinguished based on the organ from which they originate: namely the seed, the shoot, the hypocotyl/mesocotyl, and other roots (Zobel and Waisel, 2010). The last class is lateral roots, which form in most plants the majority of the root length, but not necessarily of the root weight, as lateral roots have smaller diameter. Lateral roots start with the formation of lateral root primordia, closely behind the root tip of the parent root. These primordia undergo nine distinguishable steps, of which the last step is the emergence from the cortex of the parent root just behind the zone of elongation, usually only a few days after the first cell divisions that lead to their formation (Malamy and Benfey, 1997). However, not all primordia develop into lateral roots; some stay dormant (Dubrovsky et al., 2006), although dormancy of primordia may not occur in maize (Zea mays; Jordan et al., 1993; Ploshchinskaia et al., 2002). The final number of lateral roots is thereby dependent on the rate of primordia formation as well as the percentage of primordia that develop into lateral roots. This process of primordia formation and lateral root emergence is being studied intensively, including the genes that are activated during the different steps and the hormones regulating the process (López-Bucio et al., 2003; Dubrovsky et al., 2006; Osmont et al., 2007; Péret et al., 2009; Lavenus et al., 2013). Significant genotypic variation in the density of lateral roots has been observed, ranging from no lateral roots to 41 roots cm⁻¹ in maize (Trachsel et al., 2010; Lynch, 2013). This suggests that clear tradeoffs exist for the development of lateral roots and that these genotypes have preprogrammed growth patterns that are adaptive to specific environments. While some of the variation for lateral root branching density (LRBD) that has been observed across environments, for example by Trachsel et al. (2010), is constitutive, many genotypes have strong plasticity responses of LRBD to variations in soil fertility (Zhu et al., 2005a; Osmont et al., 2007). Both the nutrient and carbon status of the plant and the local nutrient environment of the (parent) root tip influence LRBD. Many studies have documented these plasticity responses, and others have tried to unravel parts of the sensing and signaling pathways that regulate LRBD. The utility of root proliferation into a nutrient patch has been studied and debated (Robinson et al., 1999; Hodge, 2004), but much less so the utility of having fewer or more branches across the whole root system. Our understanding of the adaptive significance of variation in LRBD among genotypes is thereby limited, with many studies not accounting for relevant tradeoffs. In this study, we integrate several functional aspects of LRBD with respect to nutrient acquisition, root competition, and internal resource costs and quantify these functional aspects using the functional structural plant model SimRoot. SimRoot simulates plant growth with explicit representation of root architecture in three dimensions (Fig. 1; Supplemental Movie S1). The model focuses on the resource acquisition by the root system and carbon fixation by the shoot while estimating the resource utilization and requirements by all the different organs.

Table I.

Minimum, maximum, and median LRBD in different populations phenotyped by various researchers at several locations in the worldLocations are as follows: D, Juelich, Germany; PA, State College, PA; and SA, Alma, South Africa. Some of the experiments included nutrient treatments: LN, low nitrogen availability; and LP, low phosphorus availability. Data were collected by counting the number of roots along a nodal root segment. Data were supplied by the person indicated under source: H.S., H. Schneider; L.Y., L. York; A.Z., A. Zhan; and J.P., J.A. Postma. WiDiv, Wisconsin Diversity panel; IBM, intermated B73 × Mo17; NAM, nested association mapping.

Focus Issue on Roots: Branching Out in Roots: Uncovering Form,Function, and Regulation

Root branching is critical for plants to secure anchorage and ensure the supply of water, minerals, and nutrients. To date, research on root branching has focused on lateral root development in young seedlings. However, many other programs of postembryonic root organogenesis exist in angiosperms. In cereal crops, the majority of the mature root system is composed of several classes of adventitious roots that include crown roots and brace roots. In this Update, we initially describe the diversity of postembryonic root forms. Next, we review recent advances in our understanding of the genes, signals, and mechanisms regulating lateral root and adventitious root branching in the plant models Arabidopsis (Arabidopsis thaliana), maize (Zea mays), and rice (Oryza sativa). While many common signals, regulatory components, and mechanisms have been identified that control the initiation, morphogenesis, and emergence of new lateral and adventitious root organs, much more remains to be done. We conclude by discussing the challenges and opportunities facing root branching research.Branching through lateral and adventitious root formation represents an important element of the adaptability of the root system to its environment. Both are regulated by nutrient and hormonal signals (Bellini et al., 2014; Giehl and von Wirén, 2014), which act locally to induce or inhibit root branching. The net effect of these adaptive responses is to increase the surface area of the plant root system in the most important region of the soil matrix for resource capture (e.g. surface layers for phosphorus uptake and deeper layers for nitrate uptake) or to secure anchorage. Different species use different combinations of lateral or adventitious roots to achieve this, with lateral roots dominating the root system of eudicots while adventitious (crown and brace) roots dominate the root system of monocots, including in cereal crops.Our understanding of the mechanisms controlling lateral and adventitious root development has accelerated in recent years, primarily through research on model plants. The simple anatomy and the wealth of genetic resources in Arabidopsis (Arabidopsis thaliana) have greatly aided embryonic and postembryonic root developmental studies (De Smet et al., 2007; Péret et al., 2009a; Fig. 1, A and E). Nevertheless, impressive recent progress has been made studying root branching in other crop species, notably cereals such as maize (Zea mays) and rice (Oryza sativa).Open in a separate windowFigure 1.A to D, Schematics showing diversity in root system architecture at both seedling (left) and mature (right) stages in eudicots (A and C) and monocots (B and D). A, Arabidopsis root system. B, Maize root system. C, Tomato root system (for clarity, stem-derived adventitious roots are only shown in the labeled region). D, Wheat root system. E and F, Cross sections of emerging lateral root primordia in Arabidopsis (E) and rice (F). E and F are adapted from Péret et al. (2009b).In this Update, we initially describe the diversity of postembryonic root forms in eudicots and monocots (Fig. 1). Next, we highlight recent advances in our understanding of the genes, signals, and mechanisms regulating lateral root and adventitious root branching in Arabidopsis, rice, and maize. Due to space limits, we cannot provide an exhaustive review of this subject area, focusing instead on recent research advances. However, we direct readers to several recent in-depth reviews on lateral root (Lavenus et al., 2013; Orman-Ligeza et al., 2013) and adventitious root development (Bellini et al., 2014).     

Focus Issue on Roots: Natural Variation of Root Traits: From Development to Nutrient Uptake

The root system has a crucial role for plant growth and productivity. Due to the challenges of heterogeneous soil environments, diverse environmental signals are integrated into root developmental decisions. While root growth and growth responses are genetically determined, there is substantial natural variation for these traits. Studying the genetic basis of the natural variation of root growth traits can not only shed light on their evolution and ecological relevance but also can be used to map the genes and their alleles responsible for the regulation of these traits. Analysis of root phenotypes has revealed growth strategies and root growth responses to a variety of environmental stimuli, as well as the extent of natural variation of a variety of root traits including ion content, cellular properties, and root system architectures. Linkage and association mapping approaches have uncovered causal genes underlying the variation of these traits.Since their advent more than 400 million years ago, vascular plants have drastically transformed the land surface of our planet and facilitated the dense colonization of its land masses (Algeo and Scheckler, 1998; Gibling and Davies, 2012). Key to this was the evolution of root systems that enable plants to forage their environment for nutrients and water and anchor themselves tightly in the soil substrate. Soils are very heterogeneous environments, and because of the constant need to optimize root distribution in the soil according to sometimes conflicting parameters, root growth and development are some of the most plastic traits in plants. This plasticity is guided by environmental information that is integrated into decisions regarding how fast and in which direction to grow and where and when to place new lateral roots (LRs; Malamy and Ryan, 2001; Malamy, 2005). The distribution and function of roots are of crucial importance for plants. In fact, they are considered the most limiting factors for plant growth in almost all natural ecosystems (Den Herder et al., 2010). Not surprisingly, the plant root system plays a major role in yield and overall plant productivity (Lynch, 1995; Den Herder et al., 2010).The extent of plasticity is determined by genetic components (Pigliucci, 2005). For instance, one ecotype of a plant species may be able to increase root growth rate on a certain stimulus, whereas another ecotype lacks this characteristic (Gifford et al., 2013). The genetic components that govern traits in different ecotypes represent the outcome of adaptation arising from the selection of those traits that allow better adapted populations to reproduce more successfully (higher fitness) than less well-adapted populations (Trontin et al., 2011; Savolainen, 2013). Although local adaptation is common in plants and animals, its genetic basis is still poorly understood (Savolainen et al., 2013). Traits that drive local adaptation are often quantitative traits shaped by multiple genes. Therefore, phenotypic differences are often caused by allelic variation at several loci, each of them making small contributions to the trait (Weigel and Nordborg, 2005; Rockman, 2012). Studying the genetic basis of the natural variation of traits cannot only shed light on the evolution of these traits and their ecological relevance but also, can be used to map the genes responsible for the regulation of these traits.Most efforts to study intraspecies genetic variation to find trait-governing genes or identify useful traits have been conducted in crop species and the model plant Arabidopsis (Arabidopsis thaliana). Whereas in crop species, traits that are used have been subjected to human-directed selection during domestication, often with the aim of increasing productivity, in Arabidopsis, it is mostly natural selection that is examined. Arabidopsis is widely distributed around the world, inhabiting diverse environments that include beaches, rocky slopes, riverbanks, roadsides, and areas surrounding agriculture fields (Horton et al., 2012). A large number of accessions has been collected over the past decades from locations all over the world and made available to the scientific community. Importantly, these accessions of Arabidopsis exhibit a striking diversity of phenotypic variation of morphology and physiology (Koornneef et al., 2004) and can be used to understand the genetic and molecular bases of traits using quantitative genetics. Variations of traits are measured in a panel of genetically distinct plant strains and then correlated with the occurrence of genetic markers in these plants. Linked or associated genome regions can eventually be identified, and additional analysis can be conducted to find the causal genes. Self-fertilizing species, such as Arabidopsis, are particularly suited for such approaches, because they can be maintained as inbred lines and therefore, need to be genotyped only one time, after which they can be phenotyped multiple times. In the past, natural variation has been used to map causal genes mainly by using recombinant inbred lines (RILs) approaches; these are very powerful but lack a high mapping resolution, and they can only capture a very small subset of the allelic diversity (Korte and Farlow, 2013). However, the advent of new and cheap large-scale genotyping and sequencing technologies has enabled large-scale, high-resolution genotyping (Horton et al., 2012) and even the complete sequencing of a large number of plant strains (http://1001genomes.org; 3,000 Rice Genomes Project, 2014). With these data, genome-wide association studies (GWASs) for identification of alleles responsible for many different quantitative traits have become feasible (Weigel, 2012). In these studies, traits of a large number of accessions are measured and subsequently associated with genotyped markers, most frequently single-nucleotide polymorphism. Although GWASs are a very powerful tool and in principle, allow for a high mapping accuracy, a notable disadvantage is that the complexity of the population structure can confound these studies. However, there has been remarkable progress addressing this issue (Atwell et al., 2010; Segura et al., 2012).In this review, we highlight recent progress in understanding the genetic bases of natural variation of growth, development, and physiology of the root system. After briefly explaining how root growth and development give rise to the root system architecture (RSA), we highlight natural variation and what has been learned from it for fundamental processes in root growth and development, root growth responses to nutrient availability, and ion uptake and homeostasis.     

Focus Issue on Roots: Visualization of Root Water Uptake: Quantification of Deuterated Water Transport in Roots Using Neutron Radiography and Numerical Modeling

Our understanding of soil and plant water relations is limited by the lack of experimental methods to measure water fluxes in soil and plants. Here, we describe a new method to noninvasively quantify water fluxes in roots. To this end, neutron radiography was used to trace the transport of deuterated water (D₂O) into roots. The results showed that (1) the radial transport of D₂O from soil to the roots depended similarly on diffusive and convective transport and (2) the axial transport of D₂O along the root xylem was largely dominated by convection. To quantify the convective fluxes from the radiographs, we introduced a convection-diffusion model to simulate the D₂O transport in roots. The model takes into account different pathways of water across the root tissue, the endodermis as a layer with distinct transport properties, and the axial transport of D₂O in the xylem. The diffusion coefficients of the root tissues were inversely estimated by simulating the experiments at night under the assumption that the convective fluxes were negligible. Inverse modeling of the experiment at day gave the profile of water fluxes into the roots. For a 24-d-old lupine (Lupinus albus) grown in a soil with uniform water content, root water uptake was higher in the proximal parts of lateral roots and decreased toward the distal parts. The method allows the quantification of the root properties and the regions of root water uptake along the root systems.Understanding how and where plant roots extract water from soil remains an open question for both plant and soil scientists. One of the open questions concerns the locations of water uptake along the root system (Frensch and Steudle, 1989; Doussan et al., 1998; Steudle, 2000; Zwieniecki et al., 2003; Javaux et al., 2008). A motivation of these studies is that a better prediction of root water uptake may help to optimize irrigation and identify optimal traits to capture water. Despite its importance, there is little experimental information on the spatiotemporal distribution of the uptake zone along roots growing in soil. The lack of experimental data is largely due to the technical difficulties in measuring water fluxes in soils and roots.Quantitative information on the rate and location of root water uptake along roots growing in soil is needed to better understand the function of roots in extracting water from the soil and tolerating drought events. Such information may show which parts of roots are more involved in water extraction and how root hydraulic properties change during root growth and exposure to water-limiting conditions. For instance, it is not clear how root anatomy and the hydraulic conductivity of roots change as the soil becomes dry or the transpiration demand increases. Quantitative information of the location of root water uptake can be used to estimate the spatial distribution of hydraulic conductivities along roots. This information is needed to parameterize the most recent and advanced models of root water uptake, such as those of Doussan et al. (1998) and Javaux et al. (2008).Most of the experimental information on the spatial distribution of water uptake is limited to roots grown in hydroponic and aeroponic cultures (Frensch and Steudle, 1989; Varney and Canny, 1993; Zwieniecki et al., 2003; Knipfer and Fricke, 2010a). These investigations substantially improved our knowledge of the mechanism of water transport in roots. However, roots grown in hydroponic and aeroponic cultures may have different properties than those of roots grown in soils. As the soil dries, the hydraulic conductivity of roots and of the root-soil interface changes and likely affects the profile of root water uptake (Blizzard and Boyer, 1980; Nobel and Cui, 1992; Huang and Nobel, 1993; McCully, 1995; North and Nobel, 1997; Carminati et al., 2011; Knipfer et al., 2011; McLean et al., 2011; Carminati, 2012).New advances in imaging techniques are opening new avenues for noninvasively studying water uptake by roots in soils (Doussan et al., 1998; Garrigues et al., 2006; Javaux et al., 2008; Pohlmeier et al., 2008; Moradi et al., 2011). Imaging methods such as x-ray computed tomography, light transmission imaging, NMR, and computed neutron radiography allow quantifying the changes of water content in the root zone with different accuracy and spatial resolution. However, due to the concomitant soil water redistribution, the local changes in soil water content are not trivially related to root uptake. Consequently, the estimation of root water uptake requires coupling the imaging methods with the modeling of water flow in the soil, which, in turn, requires accurate information on the hydraulic properties of soil and roots. An additional complexity is represented by the peculiar and only partly understood hydraulic properties of the soil in the vicinity of the roots, the so-called rhizosphere.The hydraulic properties of the rhizosphere are influenced by root and microorganism activity, soil compaction due to root growth, and the formation of air-filled gaps between soil and roots when roots shrink (Nye, 1994; North and Nobel, 1997; Carminati et al., 2010; Aravena et al., 2011; Moradi et al., 2011; Carminati, 2013; Zarebanadkouki and Carminati, 2014). To date, it has been technically difficult to quantify the hydraulic properties of the rhizosphere. Carminati et al. (2011) showed that the hydraulic properties of the first 1 to 2 mm near the root affect the profile of water content and water potential toward the root.Recently, we introduced a novel method to noninvasively trace the flow of water in soil and roots (Zarebanadkouki et al., 2012, 2013). The method combines neutron radiography and the injection of deuterated water (D₂O). Neutron radiography is an imaging technique that allows one to quantify the water distribution in thin soil samples with high accuracy and spatial resolution (Moradi et al., 2008). D₂O is an isotope of normal water. Its chemical and physical properties are similar to those of water, but in contrast to water, it is almost transparent in neutron transmission imaging (Matsushima et al., 2012). This property makes D₂O an excellent tracer for neutron imaging of water flow.In our previous experiments (Zarebanadkouki et al., 2012, 2013), D₂O was injected next to selected roots and its transport was monitored using time-series neutron radiography with a spatial resolution of 150 μm and a temporal resolution of 10 s for a duration of 2 h. We grew lupine (Lupinus albus) in aluminum containers (width of 25 cm, height of 30 cm, and thickness of 1 cm) filled with a sandy soil. The soil was partitioned into different compartments with a 1-cm layer of coarse sand acting as a capillary barrier (three vertical and four horizontal layers placed at regular intervals). The capillary barriers limited the transport of D₂O into a given region of soil and facilitated the quantification of D₂O transport into the roots. Figure 1 shows selected neutron radiographs of D₂O injection during the day and night. This figure is modified from Zarebanadkouki et al. (2013). The radiographs show that (1) the radial transport of D₂O into the roots was faster during the day than during the night and (2) the axial transport of D₂O along the roots was visible only during the day, while it was negligible at night. The differences between nighttime and daytime measurements were caused by the net flow of water induced by transpiration.Open in a separate windowFigure 1.Neutron radiographs of two samples after injection of 4 mL of D₂O during the day (A and B) and during the night (C and D). D₂O was injected in one compartment during the nighttime and in two compartments during the daytime. The images show the differences between the actual radiographs at time t and the radiograph before injection (t = 0). Brighter colors indicate lower neutron attenuation and higher D₂O-water ratio. The images show that (1) the transport of D₂O was faster during the day than during the night and (2) D₂O moved axially beyond the capillary barrier toward the shoot only during the day. Images are closeups of the original field of view of 15.75 × 15.75 cm showing the distribution of D₂O in the soil and root after D₂O injection. Figures are extracted from Zarebanadkouki et al. (2013). (A neutron radiograph of the whole sample used for daytime measurement is given in Figure 9.) [See online article for color version of this figure.]The interpretation of tracing experiments with D₂O in which water and D₂O are mixed is not straightforward (Carminati and Zarebanadkouki, 2013; Warren et al., 2013a, 2013b). To determine the convective fluxes from the radiographs, Zarebanadkouki et al. (2012, 2013) introduced a diffusion-convection model of D₂O transport in roots. The model was solved analytically. The model described the increase of the average D₂O concentration in the root with a double-exponential equation, in which the rate constants of the first and second phases were related to the transport of D₂O into the cortex and the stele of the roots. Although the model included important details of the root structure, such as different pathways of water across the root tissue, the diffusion of D₂O across the root tissue was strongly simplified. In particular, our previous model assumed that as soon as the roots were immersed in D₂O, the apoplastic free space of the root cortex was instantaneously saturated with D₂O. In other words, we assumed that all cortical cells and the root endodermis were simultaneously immersed in an identical concentration of D₂O equal to that of the soil. Additionally, we assumed that D₂O concentration inside the cortical cell and the root stele was uniform (well-stirred compartment).Although the radiographs clearly showed a significant axial transport of D₂O beyond the capillary barrier during the daytime (Fig. 1B), the model of Zarebanadkouki et al. (2013) was not capable of simulating it appropriately. Indeed, our previous model could only simulate the changes in D₂O concentration in the root segments immersed in D₂O. Since the concentration of D₂O in the root segment beyond the capillary barrier carries additional information on the axial and radial fluxes along the roots, we decided to modify our model to include such information.Another approximation of the previous model was the assumption that the radial water flow to the root was uniform along the root segment immersed in D₂O. However, Zarebanadkouki et al. (2013) found significant variations in root water uptake along the roots and suggested that root water uptake should be measured with a better spatial resolution.The objective of this study was to provide an adequate model to interpret tracing experiments with D₂O. We developed two different models to describe the transport of D₂O into roots. (1) In the first model, we described the transport of D₂O into the roots by taking into account the different pathways of water across the root tissue (i.e. the apoplastic and the cell-to-cell pathways). Although this model captures the complexity of the root structure, it requires several parameters, such as the ratio of the water flow in the apoplast over the water flow in the cell-to-cell pathway. We refer to this model as the composite transport model. (2) In the second model, we simplified the root tissue into a homogenous flow domain comprising both pathways. The latter model is a simplification of the complex root anatomy, but it has the advantage of requiring fewer parameters. We refer to this model as the simplified model.In the next sections, we introduce the two modeling approaches and run a sensitivity analysis to test whether the transport of D₂O into roots is sensitive to the parameters of the composite transport model. The question was, do we need the composite transport model to accurately estimate the water flow into the roots based on the experiments with neutron radiography? Or alternatively, can we use the simplified model to estimate the fluxes without the need of introducing several parameters?Our final goal was to develop a numerical procedure to extract quantitative information on the water fluxes and the root hydraulic properties based on the tracing experiments with neutron radiography. Based on the results of the sensitivity analysis, we chose the simplified model to simulate the experiments. By fitting the observed D₂O transport into the roots, we calculated the profiles of water flux across the roots of a 24-d-old lupine as well as the diffusion permeability of its roots.     

氮磷添加对麦冬根部养分浓度及其化学计量比的影响

以城市地被植物麦冬(Ophiopogon japonicus(Thunb.)Ker-Gawl.)为研究对象,研究了土壤中添加氮(N)磷(P)后对植物根部N、P养分及其化学计量比的影响。结果表明,实验监测期间(10-12月),麦冬根部N浓度平均值表现为N(5gm-2) P(1gm-2)处理>N(5gm-2)处理=P(1gm-2)处理>对照,根部P浓度和N:P比差异不显著(P>0.1)。而监测期间的N、P月份动态结果表明,同对照相比,N处理、P处理和N P处理的麦冬根部N、P浓度和N:P比的差异性均表现为监测前期(10月)较大,中后期(11-12月)较小的变化趋势。这说明麦冬能保持其根部N、P水平的稳定性,具有较强的应对N沉降的能力,且补充P肥可增强这种能力。因此,麦冬可在大气沉降严重的地区应用和推广。     

Focus Issue on Calcium Signaling: Calcium and Reactive Oxygen Species Rule the Waves of Signaling

Calcium signaling and reactive oxygen species signaling are directly connected, and both contribute to cell-to-cell signal propagation in plants.Calcium (Ca²⁺) is an important second messenger with diverse functions not only in mammals but also in plants. It is released in response to a variety of stimuli like biotic and abiotic stresses and facilitates a tight regulation of response reactions as well as of developmental processes (Sanders et al., 2002; Steinhorst and Kudla, 2012). Ca²⁺ accumulation events are characterized by distinct temporal and spatial features, and they can vary in terms of amplitude, frequency, and duration (Webb et al., 1996; Scrase-Field and Knight, 2003; Dodd et al., 2010; Kudla et al., 2010). Spatially defined Ca²⁺ signals can be generated due to the especially slow diffusion rate of the Ca²⁺ ion in the cytoplasm in combination with tightly regulated release and uptake from and into different intracellular stores and the apoplast. Together, these characteristics encode information about particular stimuli, for example, drought stress that is presented to the cell as so-called Ca²⁺ signatures (Webb et al., 1996). This information has to be decoded and transmitted by a signaling machinery in order to initiate adequate response reactions, for example, stomatal closure (Allen et al., 2000, 2001; Sanders et al., 2002). Ca²⁺ signatures can be sensed by proteins that bind Ca²⁺ via helix-loop-helix EF-hand motifs. Arabidopsis (Arabidopsis thaliana) possesses at least 250 putative EF-hand proteins, 100 of which have been classified as Ca²⁺ sensor proteins (Day et al., 2002; Hashimoto and Kudla, 2011). Given that each member of this intricate set of Ca²⁺ sensor proteins can exhibit characteristic expression and subcellular localization profiles as well as distinct Ca²⁺ affinities, plants are equipped with a complex signal-decoding machinery to process a wide range of different Ca²⁺ signals (Batistič and Kudla, 2004; Batistič and Kudla, 2010). Ca²⁺ functions in concert with other important second messengers like reactive oxygen species (ROS). ROS can be generated in a controlled manner by several types of enzymes, such as NADPH oxidases, in order to contribute to pathogen defense and cell signaling. Recent findings point to direct connections between ROS and Ca²⁺ signaling pathways that enable cell-to-cell communication and thereby long-distance transmission of signals in plants. In this Update, we focus on new findings in the field of plant Ca²⁺ signaling during the past 3 years since the status of the field was discussed in comprehensive reviews (Dodd et al., 2010; Kudla et al., 2010; Mazars et al., 2011; Reddy et al., 2011) and put special emphasis on the contribution of a plant-specific Ca²⁺ signaling network to deciphering defined Ca²⁺ signals and its integration with ROS signaling.