Assessing variability in root traits of wild Lupinus angustifolius germplasm: basis for modelling root system structure

Background and aims

Intra-specific variation in root system architecture and consequent efficiency of resource capture by major crops has received recent attention. The aim of this study was to assess variability in a number of root traits among wild genotypes of narrow-leafed lupin (Lupinus angustifolius L.), to provide a basis for modelling of root structure.

Methods

A subset of 111 genotypes of L. angustifolius was selected from a large germplasm pool based on similarity matrices calculated using Diversity Array Technology markers. Plants were grown for 6?weeks in the established semi-hydroponic phenotyping systems to measure the fine-scale features of the root systems.

Results

Root morphology of wild L. angustifolius was primarily dominated by the taproot and first-order branches, with the presence of densely or sparsely distributed second-order branches in the late growth stage. Large variation in most root traits was identified among the tested genotypes. Total root length, branch length and branch number in the entire root system and in the upper roots were the most varied traits (coefficient of variation CV >0.50). Over 94% of the root system architectural variation determined from the principal components analysis was captured by six components (eigenvalue >1). Five relatively homogeneous groups of genotypes with distinguished patterns of root architecture were separated by k-means clustering analysis.

Conclusions

Variability in the fine-scale features of root systems such as branching behaviour and taproot growth rates provides a basis for modelling root system structure, which is a promising path for selecting desirable root traits in breeding and domestication of wild and exotic resources of L. angustifolius for stressful or poor soil environments.     

QTL mapping and phenotypic variation for root architectural traits in maize (Zea mays L.)

Key message

QTL were identified for root architectural traits in maize.

Abstract

Root architectural traits, including the number, length, orientation, and branching of the principal root classes, influence plant function by determining the spatial and temporal domains of soil exploration. To characterize phenotypic patterns and their genetic control, three recombinant inbred populations of maize were grown for 28 days in solid media in a greenhouse and evaluated for 21 root architectural traits, including length, number, diameter, and branching of seminal, primary and nodal roots, dry weight of embryonic and nodal systems, and diameter of the nodal root system. Significant phenotypic variation was observed for all traits. Strong correlations were observed among traits in the same root class, particularly for the length of the main root axis and the length of lateral roots. In a principal component analysis, relationships among traits differed slightly for the three families, though vectors grouped together for traits within a given root class, indicating opportunities for more efficient phenotyping. Allometric analysis showed that trajectories of growth for specific traits differ in the three populations. In total, 15 quantitative trait loci (QTL) were identified. QTL are reported for length in multiple root classes, diameter and number of seminal roots, and dry weight of the embryonic and nodal root systems. Phenotypic variation explained by individual QTL ranged from 0.44 % (number of seminal roots, NyH population) to 13.5 % (shoot dry weight, OhW population). Identification of QTL for root architectural traits may be useful for developing genotypes that are better suited to specific soil environments.     

Major quantitative trait loci for seminal root morphology of wheat seedlings

Vigorous early root growth at seedling stage has been shown to be important for efficient acquisition of nutrients in wheat (Triticum aestivum L.). Identifying quantitative trait loci (QTL) for early root growth can facilitate the selection of wheat varieties with efficient nutrient use. A recombinant inbred line population derived from two Chinese wheat varieties, Xiaoyan 54 and Jing 411, was grown hydroponically at seedling stage. The maximum root length (MRL), primary root length (PRL), lateral root length (LRL), total root length (TRL), and root tip number (RN) of seminal roots were measured using the WinRHIZO Root Analyser. Numerous QTL for the investigated root traits were detected with QTL numbers varying from two to six, depending on the traits. Among them, two loci had major effects on primary (MRL and PRL) and lateral (LRL and RN) root parameters, respectively. The QTL (namely qTaLRO-B1) between Xgwm210 and Xbarc1138.2 on chromosome 2B explained 68.0 and 59.0% of phenotypic variations in MRL and PRL, respectively; the major QTL between Xgwm570 and Xgwm169.2 on chromosome 6A explained 30.5 and 24.5% of phenotypic variations in LRL and RN, respectively. These two major loci showed linkage with previous reported QTL for yield component and nutrient uptake. Detailed analysis of qTaLRO-B1 indicated that the positive allele of qTaLRO-B1 showed dominance over the negative allele, which showed impairment in primary root elongation. The existence of major QTL for root trait and their linkage with agronomic traits and nutrient uptake will facilitate the design of root morphology for better yield performance and efficient nutrient use.     

Genome-wide association mapping and agronomic impact of cowpea root architecture

Key message

Genetic analysis of data produced by novel root phenotyping tools was used to establish relationships between cowpea root traits and performance indicators as well between root traits and Striga tolerance.

Abstract

Selection and breeding for better root phenotypes can improve acquisition of soil resources and hence crop production in marginal environments. We hypothesized that biologically relevant variation is measurable in cowpea root architecture. This study implemented manual phenotyping (shovelomics) and automated image phenotyping (DIRT) on a 189-entry diversity panel of cowpea to reveal biologically important variation and genome regions affecting root architecture phenes. Significant variation in root phenes was found and relatively high heritabilities were detected for root traits assessed manually (0.4 for nodulation and 0.8 for number of larger laterals) as well as repeatability traits phenotyped via DIRT (0.5 for a measure of root width and 0.3 for a measure of root tips). Genome-wide association study identified 11 significant quantitative trait loci (QTL) from manually scored root architecture traits and 21 QTL from root architecture traits phenotyped by DIRT image analysis. Subsequent comparisons of results from this root study with other field studies revealed QTL co-localizations between root traits and performance indicators including seed weight per plant, pod number, and Striga (Striga gesnerioides) tolerance. The data suggest selection for root phenotypes could be employed by breeding programs to improve production in multiple constraint environments.

     

Genome-wide association analysis of seedling root development in maize (Zea mays L.)

Background

Plants rely on the root system for anchorage to the ground and the acquisition and absorption of nutrients critical to sustaining productivity. A genome wide association analysis enables one to analyze allelic diversity of complex traits and identify superior alleles. 384 inbred lines from the Ames panel were genotyped with 681,257 single nucleotide polymorphism markers using Genotyping-by-Sequencing technology and 22 seedling root architecture traits were phenotyped.

Results

Utilizing both a general linear model and mixed linear model, a GWAS study was conducted identifying 268 marker trait associations (p ≤ 5.3×10^-7). Analysis of significant SNP markers for multiple traits showed that several were located within gene models with some SNP markers localized within regions of previously identified root quantitative trait loci. Gene model GRMZM2G153722 located on chromosome 4 contained nine significant markers. This predicted gene is expressed in roots and shoots.

Conclusion

This study identifies putatively associated SNP markers associated with root traits at the seedling stage. Some SNPs were located within or near (<1 kb) gene models. These gene models identify possible candidate genes involved in root development at the seedling stage. These and respective linked or functional markers could be targets for breeders for marker assisted selection of seedling root traits.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1226-9) contains supplementary material, which is available to authorized users.     

Genetic architecture of aerial and root traits in field-grown grafted grapevines is largely independent

Key message

QTLs were identified for traits assessed on field-grown grafted grapevines. Root number and section had the largest phenotypic variance explained. Genetic control of root and aerial traits was independent.

Abstract

Breeding new rootstocks for perennial crops remains challenging, mainly because of the number of desirable traits which have to be combined, these traits include good rooting ability and root development. Consequently, the present study analyzes the genetic architecture of root traits in grapevine. A segregating progeny of 138 F1 genotypes issued from an inter-specific cross between Vitis vinifera cv. Cabernet-Sauvignon × V. riparia cv. Gloire de Montpellier, used as rootstock, was phenotyped in grafted plants grown for 2 years in the field. Seven traits, related to aerial and root development, were quantified. Heritability ranged between 0.44 for aerial biomass to 0.7 for root number. Total root number was related to the number of fine roots, while root biomass was related to the number of coarse roots. Significant quantitative trait loci (QTLs) were identified for all the traits studied with some of them explaining approximately 20% of phenotypic variance. Only a single QTL co-localized for root and aerial biomass. Identified QTLs for aerial-to-root biomass ratio suggest that aerial and root traits are controlled independently. Genes known to be involved in auxin signaling pathways and phosphorus nutrition, whose orthologues were previously shown to regulate root development in Arabidopsis, were located in the confidence intervals of several QTLs. This study opens new perspectives for breeding rootstocks with improved root development capacities.

     

Recovering Root System Traits Using Image Analysis Exemplified by Two-Dimensional Neutron Radiography Images of Lupine

Root system traits are important in view of current challenges such as sustainable crop production with reduced fertilizer input or in resource-limited environments. We present a novel approach for recovering root architectural parameters based on image-analysis techniques. It is based on a graph representation of the segmented and skeletonized image of the root system, where individual roots are tracked in a fully automated way. Using a dynamic root architecture model for deciding whether a specific path in the graph is likely to represent a root helps to distinguish root overlaps from branches and favors the analysis of root development over a sequence of images. After the root tracking step, global traits such as topological characteristics as well as root architectural parameters are computed. Analysis of neutron radiographic root system images of lupine (Lupinus albus) grown in mesocosms filled with sandy soil results in a set of root architectural parameters. They are used to simulate the dynamic development of the root system and to compute the corresponding root length densities in the mesocosm. The graph representation of the root system provides global information about connectivity inside the graph. The underlying root growth model helps to determine which path inside the graph is most likely for a given root. This facilitates the systematic investigation of root architectural traits, in particular with respect to the parameterization of dynamic root architecture models.Crucial factors for plant development are light quantity and quality as well as water and nutrient availability in soils. Regarding water and nutrient uptake, root architecture is the main aspect of plant productivity (Lynch, 2007; Smith and De Smet, 2012) and needs to be accurately considered when describing root processes. Currently, understanding the impact of roots and rhizosphere traits on plant resource efficiency is of highest relevance (Hinsinger et al., 2011). Development in this area will increase food security by enabling more sustainable production with reduced fertilizer input by improving cropping systems and cultivars for resource-limited environments (de Dorlodot et al., 2007).Root architectural development includes architectural, morphological, anatomical, as well as physiological traits. For the systematic investigation of such complex biological systems, mathematical modeling is inevitable (Roose and Schnepf, 2008). Ideally, experiments and theoretical models are developed to mutually support each other. In this way, models are created that include state-of-the-art knowledge and have significant parameters. There are various root architectural models incorporating a multitude of processes (Dunbabin et al., 2013) that are originally based on Pagès et al. (1989) and Diggle (1988). Generally, the parameterization of such models is difficult and demands elaborate experimental effort. In this work, we present a novel approach for recovering root system parameters based on image-analysis techniques. In this way, we simplify the systematic investigation of root architectural traits, in particular with respect to the parameterization of root system models.Imaging techniques for the visualization of soil-grown root systems in two and three dimensions include x-ray computed tomography (Heeraman et al., 1997; Tracy et al., 2010; Mooney et al., 2012), neutron radiography (NR; Oswald et al., 2008), and magnetic resonance imaging (Pohlmeier et al., 2008). NR is one of the most suitable techniques to investigate roots grown in soil, because it allows a high throughput, provides a strong contrast between roots and soil, and therefore requires little effort for image processing. A major advantage of NR as well as magnetic resonance imaging is the possibility to monitor water distribution and roots simultaneously (Menon et al., 2007; Oswald et al., 2008; Moradi et al., 2009; Carminati et al., 2010; Stingaciu et al., 2013). This is especially useful as water is a crucial factor ruling root allocation in soil (Hodge, 2010).Images of root architecture contain a huge amount of information, and image analysis helps to recover parameters describing certain root architectural and morphological traits. The majority of imaging systems for root systems are designed for two-dimensional images, such as RootReader2D (Clark et al., 2013), GiA Roots (Galkovskyi et al., 2012), SmartRoot (Lobet et al., 2011), EZ-Rhizo (Armengaud et al., 2009), and Growscreen (Nagel et al., 2012). See also Le Bot et al. (2010) for a review of available software. A starting point for image analysis is commonly a grayscale image of a root system. The first step is to create a binary image by segmentation. Further steps include skeletonization, root tracking, and data analysis. The most common segmentation method is some form of thresholding (e.g. RootReader2D, GiA Roots, SmartRoot, EZ-Rhizo; Stingaciu et al. [2013]). Other methods include the livewire algorithm (Basu and Pal, 2012) or the levelset method (RootTrak; Mairhofer et al., 2012) that determine the borders of each root. The creation of a root system skeleton is either done manually (e.g. DART; Le Bot et al., 2010) or based on morphological operators such as thinning and closing (GiA Roots, RootReader2D, EZ-Rhizo), sometimes with options for the user to correct skeleton points (EZ-Rhizo). The root tracking step can be performed manually (DART) or based on creating a graph representation of the root system combined with Dijkstra’s algorithm, a search algorithm that finds the shortest path between two nodes inside a graph (RootReader2D; Stingaciu et al. [2013]). Furthermore, algorithms can operate on the skeleton (EZ-Rhizo) or directly on the image source (SmartRoot). In SmartRoot, the user selects a root in the original image with a mouse click and then a skeletonization algorithm determines the skeleton of the selected root. The output of all root tracking algorithms is a data structure of a set of roots that stores information such as connectivity between roots and their position in space.Global traits of the root system are obtained directly from the segmented image or the skeleton (GiA Roots, RooTrak). Global traits include convex hull, network depth, network length distribution, maximum number of roots, maximum width of the root system, network length, and specific root length. Furthermore, the data structure from a root tracking procedure is used to obtain individual, local root parameters (DART, RootReader2D, EZ-Rhizo, SmartRoot). The latter are able to obtain root architectural parameters that can be used for model parameterization. An additional aspect is the dealing with dynamic data (i.e. images of the same root system taken at several times). Analysis of such sequences may lead to better insight into the development of the root system (e.g. DART, SmartRoot) or even reveal growth zones and their local growth velocities (Basu and Pal, 2012).Analysis software for two-dimensional images of soil-grown root systems currently work in a semiautomated way with respect to tracing individual roots. This requires considerable user input for larger root systems. We present a new, fully automated approach for recovering root architectural parameters from two-dimensional images of root systems. The software Root System Analyzer is, to our knowledge, the first algorithm for two-dimensional analysis of soil-grown root systems that features fully automated root tracking. Only primary roots have to be initiated manually by the user. The user is also free to initiate any laterals, but this is not mandatory. Further growth of primary roots and laterals is then tracked in a fully automated way. In addition, there is a user interface that allows for manual correction of individual roots if required. In this work, we do not go into the details about the segmentation step but focus on the root tracking step and the parameterization of a root system model (Leitner et al., 2010). The described algorithm starts with a sequence of segmented two-dimensional images showing the dynamic development of a root system. For each image, morphological operators are used for skeletonization. Based on this, a graph representation of the root system is created. A dynamic root architecture model helps to determine which edges of the graph belong to an individual root. The algorithm elongates each root at the root tip and simulates growth confined within the already existing graph representation. The increment of root elongation is calculated assuming constant growth. For each root, the algorithm finds all possible paths and elongates the root in the direction of the optimal path. In this way, each edge of the graph is assigned to one or more coherent roots. The algorithm considers the fact that new branches can only emerge after the apical zone has developed, which helps in the decision of whether the root is branching or two roots are crossing or overlapping. Image sequences of root systems are handled in such a way that the previous image is used as a starting point for the current image. This is helpful in the analysis of complex root systems as well as for retrieving dynamic parameters such as elongation rates. The algorithm is implemented in a set of Matlab m-files, which makes the code flexible so that it can easily be adjusted to specific experimental setups or mathematical models.We exemplify the approach with two-dimensional neutron radiography images of lupine (Lupinus albus) root systems grown in mesocosms filled with a sandy soil. Furthermore, we compare our approach with the approaches of SmartRoot and RootReader2D and demonstrate how our approach can be used to analyze large root systems.     

High-throughput root phenotyping screens identify genetic loci associated with root architectural traits in Brassica napus under contrasting phosphate availabilities

Background and Aims

Phosphate (Pi) deficiency in soils is a major limiting factor for crop growth worldwide. Plant growth under low Pi conditions correlates with root architectural traits and it may therefore be possible to select these traits for crop improvement. The aim of this study was to characterize root architectural traits, and to test quantitative trait loci (QTL) associated with these traits, under low Pi (LP) and high Pi (HP) availability in Brassica napus.

Methods

Root architectural traits were characterized in seedlings of a double haploid (DH) mapping population (n = 190) of B. napus [‘Tapidor’ × ‘Ningyou 7’ (TNDH)] using high-throughput phenotyping methods. Primary root length (PRL), lateral root length (LRL), lateral root number (LRN), lateral root density (LRD) and biomass traits were measured 12 d post-germination in agar at LP and HP.

Key Results

In general, root and biomass traits were highly correlated under LP and HP conditions. ‘Ningyou 7’ had greater LRL, LRN and LRD than ‘Tapidor’, at both LP and HP availability, but smaller PRL. A cluster of highly significant QTL for LRN, LRD and biomass traits at LP availability were identified on chromosome A03; QTL for PRL were identified on chromosomes A07 and C06.

Conclusions

High-throughput phenotyping of Brassica can be used to identify root architectural traits which correlate with shoot biomass. It is feasible that these traits could be used in crop improvement strategies. The identification of QTL linked to root traits under LP and HP conditions provides further insights on the genetic basis of plant tolerance to P deficiency, and these QTL warrant further dissection.     

Fine root tradeoffs between nitrogen concentration and xylem vessel traits preclude unified whole‐plant resource strategies in Helianthus

Recent work suggests variation in plant growth strategies is governed by a tradeoff in resource acquisition and use, ranging from a rapid resource acquisition strategy to a resource‐conservative strategy. While evidence for this tradeoff has been found in leaves, knowledge of root trait strategies, and whether they reflect adaptive differentiation across environments, is limited. In the greenhouse, we investigated variation in fine root morphology (specific root length and tissue density), chemistry (nitrogen concentration and carbon:nitrogen), and anatomy (root cross‐sectional traits) in populations of 26 Helianthus species and sister Phoebanthus tenuifolius. We also compared root trait variation in this study with leaf trait variation previously reported in a parallel study of these populations. Root traits varied widely and exhibited little phylogenetic signal, suggesting high evolutionary lability. Specific root length and root tissue density were weakly negatively correlated, but neither was associated with root nitrogen, providing little support for a single axis of root trait covariation. Correlations between traits measured in the greenhouse and native site characteristics were generally weak, suggesting a variety of equally viable root trait combinations exist within and across environments. However, high root nitrogen was associated with lower xylem vessel number and cross‐sectional area, suggesting a tradeoff between nutrient investment and water transport capacity. This led to correlations between root and leaf traits that were not always consistent with an acquisition–conservation tradeoff at the whole‐plant level. Given that roots must balance acquisition of water and nutrients with functions like anchorage, exudation, and microbial symbioses, the varied evidence for root trait covariation likely reflects the complexity of interacting selection pressures belowground. Similarly, the lack of evidence for a single acquisition–conservation tradeoff at the whole‐plant level likely reflects the vastly different selection pressures shaping roots and leaves, and the resources they are optimized to obtain.     

Root architecture and hydraulic conductance in nutrient deprived Pistacia lentiscus L. seedlings

Plants respond to low nutrient availability by modifying root morphology and root system topology. Root responses to nitrogen (N) and phosphorus (P) limitation may affect plant capacity to withstand water stress. But studies on the effect of nutrient availability on plant ability to uptake and transport water are scarce. In this study, we assess the effect of nitrogen and phosphorus limitation on root morphology and root system topology in Pistacia lentiscus L seedlings, a common Mediterranean shrub, and relate these changes to hydraulic conductivity of the whole root system. Nitrogen and phosphorus deprivation had no effect on root biomass, but root systems were more branched in nutrient limited seedlings. Total root length was higher in seedlings subjected to phosphorus deprivation. Root hydraulic conductance decreased in nutrient-deprived seedlings, and was related to the number of root junctions but not to other architectural traits. Our study shows that changes in nutrient availability affect seedling water use by modifying root architecture. Changes in nutrient availability should be taken into account when evaluating seedling response to drought.     

Contrasting root behaviour in two grass species: a test of functionality in dynamic heterogeneous conditions

Root systems are highly plastic as they express a range of responses to acquire patchily distributed nutrients. However, the ecological significance of placing roots selectively in nutrient hotspots is still unclear. Here, we investigate under what conditions selective root placement may be a significant functional trait that determines belowground competitive ability. We studied two grasses differing in root foraging behaviour, Festuca rubra and Anthoxanthum odoratum. The plants were grown in stable and more dynamic heterogeneous environments, by switching nutrient patches halfway through the experiment. A. odoratum was a factor of two less selective in placing its roots into nutrient-rich patches than F. rubra. A. odoratum produced overall higher root length densities with higher specific root length than F. rubra and acquired more nutrients. A. odoratum appeared to be the superior competitor, irrespective of the nutrient dynamics. Our results suggest that root behaviour consisting of producing high root length densities at relatively low biomass investments can be a more effective foraging strategy than placing roots selectively in nutrient hotspots. When understanding the functionality of root traits among different species, specific root length may play a key role.     

Root hairs are the most important root trait for rhizosheath formation of barley (Hordeum vulgare), maize (Zea mays) and Lotus japonicus (Gifu)

Background and AimsRhizosheaths are defined as the soil adhering to the root system after it is extracted from the ground. Root hairs and mucilage (root exudates) are key root traits involved in rhizosheath formation, but to better understand the mechanisms involved their relative contributions should be distinguished.MethodsThe ability of three species [barley (Hordeum vulgare), maize (Zea mays) and Lotus japonicus (Gifu)] to form a rhizosheath in a sandy loam soil was compared with that of their root-hairless mutants [bald root barley (brb), maize root hairless 3 (rth3) and root hairless 1 (Ljrhl1)]. Root hair traits (length and density) of wild-type (WT) barley and maize were compared along with exudate adhesiveness of both barley and maize genotypes. Furthermore, root hair traits and exudate adhesiveness from different root types (axile versus lateral) were compared within the cereal species.Key ResultsPer unit root length, rhizosheath size diminished in the order of barley > L. japonicus > maize in WT plants. Root hairs significantly increased rhizosheath formation of all species (3.9-, 3.2- and 1.8-fold for barley, L. japonicus and maize, respectively) but there was no consistent genotypic effect on exudate adhesiveness in the cereals. While brb exudates were more and rth3 exudates were less adhesive than their respective WTs, maize rth3 bound more soil than barley brb. Although both maize genotypes produced significantly more adhesive exudate than the barley genotypes, root hair development of WT barley was more extensive than that of WT maize. Thus, the greater density of longer root hairs in WT barley bound more soil than WT maize. Root type did not seem to affect rhizosheath formation, unless these types differed in root length.ConclusionsWhen root hairs were present, greater root hair development better facilitated rhizosheath formation than root exudate adhesiveness. However, when root hairs were absent root exudate adhesiveness was a more dominant trait.     

Molecular mapping of genomic regions associated with wheat seedling growth under osmotic stress

A quantitative trait loci (QTL) approach was applied to dissect the genetic control of the common wheat seedling response to osmotic stress. A set of 114 recombinant inbred lines was subjected to osmotic stress from the onset of germination to the 8^th day of seedling development, induced by the presence of 12 % polyethylene glycol. Root, coleoptile and shoot length, and root/shoot length ratio were compared under stress and control conditions. In all, 35 QTL mapping to ten chromosomes, were identified. Sixteen QTL were detected in controls, 17 under stressed conditions, and two tolerance index QTL were determined. The majority of the QTL were not stress-specific. In regions on five chromosome arms (1AS, 1BL, 2DS, 5BL and 6BL) the QTL identified under stress co-mapped with QTL affecting the same trait in controls, and these were classified as seedling vigour QTL, in addition to those expressed in controls. Tolerance-related QTL were detected on four chromosome arms. A broad region on chromosome 1AL, including five QTL, with a major impact of the gene Glu-A1 (LOD 3.93) and marker locus Xksuh9d (LOD 2.91), positively affected root length under stress and tolerance index for root length, respectively. A major QTL (LOD 3.60), associated with marker locus Xcdo456a (distal part of chromosome arm 2BS) determined a tolerance index for shoot length. Three minor QTL (LOD < 3.0) for root length and root/shoot length ratio under osmotic stress were identified in the distal parts of chromosome arms 6DL (marker locus Xksud27a) and 7DL (marker locus Xksue3b). Selecting for the favourable alleles at marker loci associated with the detected QTL for growth traits may represent an efficient approach to enhance the plants’ ability to maintain the growth of roots, coleoptile and shoots in drought-prone soils at the critical early developmental stages.     

High‐throughput two‐dimensional root system phenotyping platform facilitates genetic analysis of root growth and development

High‐throughput phenotyping of root systems requires a combination of specialized techniques and adaptable plant growth, root imaging and software tools. A custom phenotyping platform was designed to capture images of whole root systems, and novel software tools were developed to process and analyse these images. The platform and its components are adaptable to a wide range root phenotyping studies using diverse growth systems (hydroponics, paper pouches, gel and soil) involving several plant species, including, but not limited to, rice, maize, sorghum, tomato and Arabidopsis. The RootReader2D software tool is free and publicly available and was designed with both user‐guided and automated features that increase flexibility and enhance efficiency when measuring root growth traits from specific roots or entire root systems during large‐scale phenotyping studies. To demonstrate the unique capabilities and high‐throughput capacity of this phenotyping platform for studying root systems, genome‐wide association studies on rice (Oryza sativa) and maize (Zea mays) root growth were performed and root traits related to aluminium (Al) tolerance were analysed on the parents of the maize nested association mapping (NAM) population.     

Genome-Wide Association Mapping of Root Traits in a Japonica Rice Panel

Rice is a crop prone to drought stress in upland and rainfed lowland ecosystems. A deep root system is recognized as the best drought avoidance mechanism. Genome-wide association mapping offers higher resolution for locating quantitative trait loci (QTLs) than QTL mapping in biparental populations. We performed an association mapping study for root traits using a panel of 167 japonica accessions, mostly of tropical origin. The panel was genotyped at an average density of one marker per 22.5 kb using genotyping by sequencing technology. The linkage disequilibrium in the panel was high (r²>0.6, on average, for 20 kb mean distances between markers). The plants were grown in transparent 50 cm × 20 cm × 2 cm Plexiglas nailboard sandwiches filled with 1.5 mm glass beads through which a nutrient solution was circulated. Root system architecture and biomass traits were measured in 30-day-old plants. The panel showed a moderate to high diversity in the various traits, particularly for deep (below 30 cm depth) root mass and the number of deep roots. Association analyses were conducted using a mixed model involving both population structure and kinship to control for false positives. Nineteen associations were significant at P<1e-05, and 78 were significant at P<1e-04. The greatest numbers of significant associations were detected for deep root mass and the number of deep roots, whereas no significant associations were found for total root biomass or deep root proportion. Because several QTLs for different traits were co-localized, 51 unique loci were detected; several co-localized with meta-QTLs for root traits, but none co-localized with rice genes known to be involved in root growth. Several likely candidate genes were found in close proximity to these loci. Additional work is necessary to assess whether these markers are relevant in other backgrounds and whether the genes identified are robust candidates.     

QTL for root angle and number in a population developed from bread wheats (Triticum aestivum) with contrasting adaptation to water-limited environments

Root architecture traits in wheat are important in deep soil moisture acquisition and may be used to improve adaptation to water-limited environments. The genetic architecture of two root traits, seminal root angle and seminal root number, were investigated using a doubled haploid population derived from SeriM82 and Hartog. Multiple novel quantitative trait loci (QTL) were identified, each one having a modest effect. For seminal root angle, four QTL (?log₁₀(P) >3) were identified on 2A, 3D, 6A and 6B, and two suggestive QTL (?log₁₀(P) >2) on 5D and 6B. For root number, two QTL were identified on 4A and 6A with four suggestive QTL on 1B, 3A, 3B and 4A. QTL for root angle and root number did not co-locate. Transgressive segregation was found for both traits. Known major height and phenology loci appear to have little effect on root angle and number. Presence or absence of the T1BL.1RS translocation did not significantly influence root angle. Broad sense heritability (h ²) was estimated as 50 % for root angle and 31 % for root number. Root angle QTL were found to be segregating between wheat cultivars adapted to the target production region indicating potential to select for root angle in breeding programs.     

Modelling root plasticity and response of narrow-leafed lupin to heterogeneous phosphorus supply

Background & Aims

Searching for root traits underpinning efficient nutrient acquisition has received increased attention in modern breeding programs aimed at improved crop productivity. Root models provide an opportunity to investigate root-soil interactions through representing the relationships between rooting traits and the non-uniform supply of soil resources. This study used simulation modelling to predict and identify phenotypic plasticity, root growth responses and phosphorus (P) use efficiency of contrasting Lupinus angustifolius genotypes to localised soil P in a glasshouse.

Methods

Two L. angustifolius genotypes with contrasting root systems were grown in cylindrical columns containing uniform soil with three P treatments (nil and 20 mg P kg^?1 either top-dressed or banded) in the glasshouse. Computer simulations were carried out with root architecture model ROOTMAP which was parameterized with root architectural data from an earlier published hydroponic phenotyping study.

Results

The experimental and simulated results showed that plants supplied with banded P had the largest root system and the greatest P-uptake efficiency. The P addition significantly stimulated root branching in the topsoil, whereas plants with nil P had relatively deeper roots. Genotype-dependent root growth plasticity in response to P supply was shown, with the greatest response to banded P.

Conclusions

Both experimental and simulation outcomes demonstrated that 1) root hairs and root proliferation increased plant P acquisition and were more beneficial in the localised P fertilisation scenario, 2) placing P deeper in the soil might be a more effective fertilisation method with greater P uptake than top dressing, and 3) the combination of P foraging strategies (including root architecture, root hairs and root growth plasticity) is important for efficient P acquisition from a localised source of fertiliser P.     

The dynamics of potassium uptake and use, leaf gas exchange and root growth throughout plant phenological development and its effects on seed yield in wheat (Triticum aestivum) on a low-K sandy soil

Background and Aims

Roots express morphological and physiological plasticity that may be adaptations for efficient nutrient capture when soil nutrients are heterogeneous in space and time. In terms of nutrient capture per unit of carbon invested in roots, morphological plasticity should be more advantageous when nutrient patches are stable in time, and physiological plasticity when nutrients are variable in time.

Methods

Here we examined both traits in two Pinus species, two Liquidambar species, two Solidago species, Ailanthus altissima and Callistephus chinesis, grown in pots where the same total level of nutrient addition was provided in a factorial experiment with different levels of spatial and temporal variability.

Results

Total plant root growth, Root/Shoot ratios and morphological plasticity were less when nutrients were temporally variable instead of stable. Physiological plasticity was more variable than morphological across treatments and species and was not predictably greater when nutrient supply was pulsed instead of constant. Large variability, especially in physiological plasticity, was observed, and physiological plasticity was greater in non-woody than in woody species.

Conclusions

Our results suggest that the two traits differ in environmental factors that control their expression, and that the nature of nutrient patchiness may have more direct impact on the evolution of morphological than physiological plasticity.     

Discovery of QTLs for water mining and water use efficiency traits in rice under water-limited condition through association mapping

Developing trait introgressed rice cultivars is essential to sustain yield under aerobic conditions. Here, we report DNA markers governing variability in root traits, water use efficiency (WUE) and other biometric traits like total leaf area by association mapping. A set of 173 diverse rice germplasm accessions were phenotyped for root traits in specially designed root structures and WUE using carbon isotope discrimination (Δ¹³C) during the monsoon season (July to October) of two consecutive years (2007 and 2008). The panel was genotyped using 291 SSR markers spanning the entire genome of rice. Root biomass varied between 1.8 and 16.3 g plant^?1 while root length between 22 and 78 cm representing significant genetic variability. Similarly, Δ¹³C varied from 18 to 23 ‰. The SSR markers showed extensive polymorphism with around 73 % of all the markers revealing polymorphism information content values more than 0.5. Model-based structure analysis using the squared-allele frequency correlations revealed six subgroups among the panel with an average LD decay of about 10–20 cM. The Benjamini–Hochberg analysis was carried out to compute the false discovery rate combined with the analysis of effective LD. A total of 82 markers were involved in 175 significant (corrected P values and Q values <0.05) marker–trait associations (MTAs) across experiment 1 and experiment 2 and for the pooled data. Out of these, 22 markers were found to be associated with more than one trait. Common markers with significant associations were discovered for root biomass, total leaf area and total biomass suggesting the interdependency of these traits. Finally, 12 markers showed significant and stable MTAs across the experiments for different traits. An in silico analysis indicated that 45 % of the MTAs overlapped with previously reported QTLs and can be used for QTL introgression through breeding.