The importance of dominance and genotype-by-environment interactions on grain yield variation in a large-scale public cooperative maize experiment

High-dimensional and high-throughput genomic, field performance, and environmental data are becoming increasingly available to crop breeding programs, and their integration can facilitate genomic prediction within and across environments and provide insights into the genetic architecture of complex traits and the nature of genotype-by-environment interactions. To partition trait variation into additive and dominance (main effect) genetic and corresponding genetic-by-environment variances, and to identify specific environmental factors that influence genotype-by-environment interactions, we curated and analyzed genotypic and phenotypic data on 1918 maize (Zea mays L.) hybrids and environmental data from 65 testing environments. For grain yield, dominance variance was similar in magnitude to additive variance, and genetic-by-environment variances were more important than genetic main effect variances. Models involving both additive and dominance relationships best fit the data and modeling unique genetic covariances among all environments provided the best characterization of the genotype-by-environment interaction patterns. Similarity of relative hybrid performance among environments was modeled as a function of underlying weather variables, permitting identification of weather covariates driving correlations of genetic effects across environments. The resulting models can be used for genomic prediction of mean hybrid performance across populations of environments tested or for environment-specific predictions. These results can also guide efforts to incorporate high-throughput environmental data into genomic prediction models and predict values in new environments characterized with the same environmental characteristics.

Utility of Climatic Information via Combining Ability Models to Improve Genomic Prediction for Yield Within the Genomes to Fields Maize Project

Genomic prediction provides an efficient alternative to conventional phenotypic selection for developing improved cultivars with desirable characteristics. New and improved methods to genomic prediction are continually being developed that attempt to deal with the integration of data types beyond genomic information. Modern automated weather systems offer the opportunity to capture continuous data on a range of environmental parameters at specific field locations. In principle, this information could characterize training and target environments and enhance predictive ability by incorporating weather characteristics as part of the genotype-by-environment (G×E) interaction component in prediction models. We assessed the usefulness of including weather data variables in genomic prediction models using a naïve environmental kinship model across 30 environments comprising the Genomes to Fields (G2F) initiative in 2014 and 2015. Specifically four different prediction scenarios were evaluated (i) tested genotypes in observed environments; (ii) untested genotypes in observed environments; (iii) tested genotypes in unobserved environments; and (iv) untested genotypes in unobserved environments. A set of 1,481 unique hybrids were evaluated for grain yield. Evaluations were conducted using five different models including main effect of environments; general combining ability (GCA) effects of the maternal and paternal parents modeled using the genomic relationship matrix; specific combining ability (SCA) effects between maternal and paternal parents; interactions between genetic (GCA and SCA) effects and environmental effects; and finally interactions between the genetics effects and environmental covariates. Incorporation of the genotype-by-environment interaction term improved predictive ability across all scenarios. However, predictive ability was not improved through inclusion of naive environmental covariates in G×E models. More research should be conducted to link the observed weather conditions with important physiological aspects in plant development to improve predictive ability through the inclusion of weather data.

Nodal root diameter and node number in maize (Zea mays L.) interact to influence plant growth under nitrogen stress

Under nitrogen limitation, plants increase resource allocation to root growth relative to shoot growth. The utility of various root architectural and anatomical phenotypes for nitrogen acquisition are not well understood. Nodal root number and root cross-sectional area were evaluated in maize in field and greenhouse environments. Nodal root number and root cross-sectional area were inversely correlated under both high and low nitrogen conditions. Attenuated emergence of root nodes, as opposed to differences in the number of axial roots per node, was associated with substantially reduced root number. Greater root cross-sectional area was associated with a greater stele area and number of cortical cell files. Genotypes that produced few, thick nodal roots rather than many, thin nodal roots had deeper rooting and better shoot growth in low nitrogen environments. Fewer nodal roots offset the respiratory and nitrogen costs of thicker diameter roots, since total nodal root respiration and nitrogen content was similar for genotypes with many, thin and few, thick nodal roots. We propose that few, thick nodal roots may enable greater capture of deep soil nitrogen and improve plant performance under nitrogen stress. Synergistic interactions between an architectural and anatomical trait may be an important strategy for nitrogen acquisition. Understanding trait interactions among different root nodes has important implications in for improving crop nutrient uptake and stress tolerance.

Multiseriate cortical sclerenchyma enhance root penetration in compacted soils

Mechanical impedance limits soil exploration and resource capture by plant roots. We examine the role of root anatomy in regulating plant adaptation to mechanical impedance and identify a root anatomical phene in maize (Zea mays) and wheat (Triticum aestivum) associated with penetration of hard soil: Multiseriate cortical sclerenchyma (MCS). We characterize this trait and evaluate the utility of MCS for root penetration in compacted soils. Roots with MCS had a greater cell wall-to-lumen ratio and a distinct UV emission spectrum in outer cortical cells. Genome-wide association mapping revealed that MCS is heritable and genetically controlled. We identified a candidate gene associated with MCS. Across all root classes and nodal positions, maize genotypes with MCS had 13% greater root lignin concentration compared to genotypes without MCS. Genotypes without MCS formed MCS upon exogenous ethylene exposure. Genotypes with MCS had greater lignin concentration and bending strength at the root tip. In controlled environments, MCS in maize and wheat was associated improved root tensile strength and increased penetration ability in compacted soils. Maize genotypes with MCS had root systems with 22% greater depth and 49% greater shoot biomass in compacted soils in the field compared to lines without MCS. Of the lines we assessed, MCS was present in 30 to 50% of modern maize, wheat, and barley cultivars but was absent in teosinte and wild and landrace accessions of wheat and barley. MCS merits investigation as a trait for improving plant performance in maize, wheat, and other grasses under edaphic stress.

Three-dimensional imaging reveals that positions of cyst nematode feeding sites relative to xylem vessels differ between susceptible and resistant wheat

Resistance conferred by the Cre8 locus of wheat prevents cereal cyst nematode feeding sites from reaching and invading root metaxylem vessels. Cyst nematodes develop syncytial feeding sites within plant roots. The success of these sites is affected by host plant resistance. In wheat (Triticum aestivum L.), 'Cre' loci affect resistance against the cereal cyst nematode (CCN) Heterodera avenae. To investigate how one of these loci (Cre8, on chromosome 6B) confers resistance, CCN-infected root tissue from susceptible (-Cre8) and resistant (+Cre8) wheat plants was examined using confocal microscopy and laser ablation tomography. Confocal analysis of transverse sections showed that feeding sites in the roots of -Cre8 plants were always adjacent to metaxylem vessels, contained many intricate 'web-like' cell walls, and sometimes 'invaded' metaxylem vessels. In contrast, feeding sites in the roots of +Cre8 plants were usually not directly adjacent to metaxylem vessels, had few inner cell walls and did not 'invade' metaxylem vessels. Models based on data from laser ablation tomography confirmed these observations. Confocal analysis of longitudinal sections revealed that CCN-induced xylem modification that had previously been reported for susceptible (-Cre8) wheat plants is less extreme in resistant (+Cre8) plants. Application of a lignin-specific stain revealed that secondary thickening around xylem vessels in CCN-infected roots was greater in +Cre8 plants than in -Cre8 plants. Collectively, these results indicate that Cre8 resistance in wheat acts by preventing cyst nematode feeding sites from reaching and invading root metaxylem vessels.

Plant roots sense soil compaction through restricted ethylene diffusion

Soil compaction represents a major challenge for modern agriculture. Compaction is intuitively thought to reduce root growth by limiting the ability of roots to penetrate harder soils. We report that root growth in compacted soil is instead actively suppressed by the volatile hormone ethylene. We found that mutant Arabidopsis and rice roots that were insensitive to ethylene penetrated compacted soil more effectively than did wild-type roots. Our results indicate that soil compaction lowers gas diffusion through a reduction in air-filled pores, thereby causing ethylene to accumulate in root tissues and trigger hormone responses that restrict growth. We propose that ethylene acts as an early warning signal for roots to avoid compacted soils, which would be relevant to research into the breeding of crops resilient to soil compaction.

A Comparative Analysis of Quantitative Metrics of Root Architecture

High throughput phenotyping is important to bridge the gap between genotype and phenotype. The methods used to describe the phenotype therefore should be robust to measurement errors, relatively stable over time, and most importantly, provide a reliable estimate of elementary phenotypic components. In this study, we use functional-structural modeling to evaluate quantitative phenotypic metrics used to describe root architecture to determine how they fit these criteria. Our results show that phenes such as root number, root diameter, and lateral root branching density are stable, reliable measures and are not affected by imaging method or plane. Metrics aggregating multiple phenes such as total length, total volume, convex hull volume, and bushiness index estimate different subsets of the constituent phenes; they however do not provide any information regarding the underlying phene states. Estimates of phene aggregates are not unique representations of underlying constituent phenes: multiple phenotypes having phenes in different states could have similar aggregate metrics. Root growth angle is an important phene which is susceptible to measurement errors when 2D projection methods are used. Metrics that aggregate phenes which are complex functions of root growth angle and other phenes are also subject to measurement errors when 2D projection methods are used. These results support the hypothesis that estimates of phenes are more useful than metrics aggregating multiple phenes for phenotyping root architecture. We propose that these concepts are broadly applicable in phenotyping and phenomics.

Root metaxylem and architecture phenotypes integrate to regulate water use under drought stress

At the genus and species level, variation in root anatomy and architecture may interact to affect strategies of drought avoidance. To investigate this idea, root anatomy and architecture of the drought-sensitive common bean (Phaseolus vulgaris) and drought-adapted tepary bean (Phaseolus acutifolius) were analyzed in relation to water use under terminal drought. Intraspecific variation for metaxylem anatomy and axial conductance was found in the roots of both species. Genotypes with high-conductance root metaxylem phenotypes acquired and transpired more water per unit leaf area, shoot mass, and root mass than genotypes with low-conductance metaxylem phenotypes. Interspecific variation in root architecture and root depth was observed where P. acutifolius has a deeper distribution of root length than P. vulgaris. In the deeper-rooted P. acutifolius, genotypes with high root conductance were better able to exploit deep soil water than genotypes with low root axial conductance. Contrastingly, in the shallower-rooted P. vulgaris, genotypes with low root axial conductance had improved water status through conservation of soil moisture for sustained water capture later in the season. These results indicate that metaxylem morphology interacts with root system depth to determine a strategy of drought avoidance and illustrate synergism among architectural and anatomical phenotypes for root function.

An Analysis of Soil Coring Strategies to Estimate Root Depth in Maize (Zea mays) and Common Bean (Phaseolus vulgaris)

A soil coring protocol was developed to cooptimize the estimation of root length distribution (RLD) by depth and detection of functionally important variation in root system architecture (RSA) of maize and bean. The functional-structural model OpenSimRoot was used to perform in silico soil coring at six locations on three different maize and bean RSA phenotypes. Results were compared to two seasons of field soil coring and one trench. Two one-sided T-test (TOST) analysis of in silico data suggests a between-row location 5 cm from plant base (location 3), best estimates whole-plot RLD/D of deep, intermediate, and shallow RSA phenotypes, for both maize and bean. Quadratic discriminant analysis indicates location 3 has ~70% categorization accuracy for bean, while an in-row location next to the plant base (location 6) has ~85% categorization accuracy in maize. Analysis of field data suggests the more representative sampling locations vary by year and species. In silico and field studies suggest location 3 is most robust, although variation is significant among seasons, among replications within a field season, and among field soil coring, trench, and simulations. We propose that the characterization of the RLD profile as a dynamic rhizo canopy effectively describes how the RLD profile arises from interactions among an individual plant, its neighbors, and the pedosphere.

Root secondary growth: an unexplored component of soil resource acquisition

BACKGROUND AND AIMS

Despite recent progress in elucidating the molecular basis of secondary growth (cambial growth), the functional implications of this developmental process remain poorly understood. Targeted studies exploring how abiotic and biotic factors affect this process, as well as the relevance of secondary growth to fitness of annual dicotyledonous crop species under stress, are almost entirely absent from the literature. Specifically, the physiological role of secondary growth in roots has been completely neglected yet entails a unique array of implications for plant performance that are distinct from secondary growth in shoot tissue.

SCOPE

Since roots are directly responsible for soil resource capture, understanding of the fitness landscape of root phenotypes is important in both basic and applied plant biology. Interactions between root secondary growth, edaphic conditions and soil resource acquisition may have significant effects on plant fitness. Our intention here is not to provide a comprehensive review of a sparse and disparate literature, but rather to highlight knowledge gaps, propose hypotheses and identify opportunities for novel and agriculturally relevant research pertaining to secondary growth of roots. This viewpoint: (1) summarizes evidence from our own studies and other published work; (2) proposes hypotheses regarding the fitness landscape of secondary growth of roots in annual dicotyledonous species for abiotic and biotic stress; and (3) highlights the importance of directing research efforts to this topic within an agricultural context.

CONCLUSIONS

Secondary growth of the roots of annual dicots has functional significance with regards to soil resource acquisition and transport, interactions with soil organisms and carbon sequestration. Research on these topics would contribute significantly toward understanding the agronomic value of secondary growth of roots for crop improvement.

Root anatomical traits contribute to deeper rooting of maize under compacted field conditions

To better understand the role of root anatomy in regulating plant adaptation to soil mechanical impedance, 12 maize lines were evaluated in two soils with and without compaction treatments under field conditions. Penetrometer resistance was 1-2 MPa greater in the surface 30 cm of the compacted plots at a water content of 17-20% (v/v). Root thickening in response to compaction varied among genotypes and was negatively associated with rooting depth at one field site under non-compacted plots. Thickening was not associated with rooting depth on compacted plots. Genotypic variation in root anatomy was related to rooting depth. Deeper-rooting plants were associated with reduced cortical cell file number in combination with greater mid cortical cell area for node 3 roots. For node 4, roots with increased aerenchyma were deeper roots. A greater influence of anatomy on rooting depth was observed for the thinner root classes. We found no evidence that root thickening is related to deeper rooting in compacted soil; however, anatomical traits are important, especially for thinner root classes.

Multiscale computational models can guide experimentation and targeted measurements for crop improvement

Computational models of plants have identified gaps in our understanding of biological systems, and have revealed ways to optimize cellular processes or organ-level architecture to increase productivity. Thus, computational models are learning tools that help direct experimentation and measurements. Models are simplifications of complex systems, and often simulate specific processes at single scales (e.g. temporal, spatial, organizational, etc.). Consequently, single-scale models are unable to capture the critical cross-scale interactions that result in emergent properties of the system. In this perspective article, we contend that to accurately predict how a plant will respond in an untested environment, it is necessary to integrate mathematical models across biological scales. Computationally mimicking the flow of biological information from the genome to the phenome is an important step in discovering new experimental strategies to improve crops. A key challenge is to connect models across biological, temporal and computational (e.g. CPU versus GPU) scales, and then to visualize and interpret integrated model outputs. We address this challenge by describing the efforts of the international Crops in silico consortium.

Multiple Integrated Root Phenotypes Are Associated with Improved Drought Tolerance

To test the hypothesis that multiple integrated root phenotypes would co-optimize drought tolerance, we phenotyped the root anatomy and architecture of 400 mature maize (Zea mays) genotypes under well-watered and water-stressed conditions in the field. We found substantial variation in all 23 root phenes measured. A phenotypic bulked segregant analysis revealed that bulks representing the best and worst performers in the field displayed distinct root phenotypes. In contrast to the worst bulk, the root phenotype of the best bulk under drought consisted of greater cortical aerenchyma formation, more numerous and narrower metaxylem vessels, and thicker nodal roots. Partition-against-medians clustering revealed several clusters of unique root phenotypes related to plant performance under water stress. Clusters associated with improved drought tolerance consisted of phene states that likely enable greater soil exploration by reallocating internal resources to greater root construction (increased aerenchyma content, larger cortical cells, fewer cortical cell files), restrict uptake of water to conserve soil moisture (reduced hydraulic conductance, narrow metaxylem vessels), and improve penetrability of hard, dry soils (thick roots with a larger proportion of stele, and smaller distal cortical cells). We propose that the most drought-tolerant-integrated phenotypes merit consideration as breeding ideotypes.

Genetic control of root architectural plasticity in maize

Root phenotypes regulate soil resource acquisition; however, their genetic control and phenotypic plasticity are poorly understood. We hypothesized that the responses of root architectural phenes to water deficit (stress plasticity) and different environments (environmental plasticity) are under genetic control and that these loci are distinct. Root architectural phenes were phenotyped in the field using a large maize association panel with and without water deficit stress for three seasons in Arizona and without water deficit stress for four seasons in South Africa. All root phenes were plastic and varied in their plastic response. We identified candidate genes associated with stress and environmental plasticity and candidate genes associated with phenes in well-watered conditions in South Africa and in well-watered and water-stress conditions in Arizona. Few candidate genes for plasticity overlapped with those for phenes expressed under each condition. Our results suggest that phenotypic plasticity is highly quantitative, and plasticity loci are distinct from loci that control phene expression in stress and non-stress, which poses a challenge for breeding programs. To make these loci more accessible to the wider research community, we developed a public online resource that will allow for further experimental validation towards understanding the genetic control underlying phenotypic plasticity.

Should Root Plasticity Be a Crop Breeding Target?

Root phenotypic plasticity has been proposed as a target for the development of more productive crops in variable environments. However, the plasticity of root anatomical and architectural responses to environmental cues is highly complex, and the consequences of these responses for plant fitness are poorly understood. We propose that root phenotypic plasticity may be beneficial in natural or low-input systems in which the availability of soil resources is spatiotemporally dynamic. Crop ancestors and landraces were selected with multiple stresses, competition, significant root loss and heterogenous resource distribution which favored plasticity in response to resource availability. However, in high-input agroecosystems, the value of phenotypic plasticity is unclear, since human management has removed many of these constraints to root function. Further research is needed to understand the fitness landscape of plastic responses including understanding the value of plasticity in different environments, environmental signals that induce plastic responses, and the genetic architecture of plasticity before it is widely adopted in breeding programs. Phenotypic plasticity has many potential ecological, and physiological benefits, but its costs and adaptive value in high-input agricultural systems is poorly understood and merits further research.

Genetic components of root architecture and anatomy adjustments to water-deficit stress in spring barley

Roots perform vital roles for adaptation and productivity under water-deficit stress, even though their specific functions are poorly understood. In this study, the genetic control of the nodal-root architectural and anatomical response to water deficit were investigated among diverse spring barley accessions. Water deficit induced substantial variations in the nodal root traits. The cortical, stele, and total root cross-sectional areas of the main-shoot nodal roots decreased under water deficit, but increased in the tiller nodal roots. Root xylem density and arrested nodal roots increased under water deficit, with the formation of root suberization/lignification and large cortical aerenchyma. Genome-wide association study implicated 11 QTL intervals in the architectural and anatomical nodal root response to water deficit. Among them, three and four QTL intervals had strong effects across seasons and on both root architectural and anatomical traits, respectively. Genome-wide epistasis analysis revealed 44 epistatically interacting SNP loci. Further analyses showed that these QTL intervals contain important candidate genes, including ZIFL2, MATE, and PPIB, whose functions are shown to be related to the root adaptive response to water deprivation in plants. These results give novel insight into the genetic architectures of barley nodal root response to soil water deficit stress in the fields, and thus offer useful resources for root-targeted marker-assisted selection.

Genetic control of root anatomical plasticity in maize

Root anatomical phenes have important roles in soil resource capture and plant performance; however, their phenotypic plasticity and genetic architecture is poorly understood. We hypothesized that (a) the responses of root anatomical phenes to water deficit (stress plasticity) and different environmental conditions (environmental plasticity) are genetically controlled and (b) stress and environmental plasticity are associated with different genetic loci than those controlling the expression of phenes under water-stress and well-watered conditions. Root anatomy was phenotyped in a large maize (Zea mays L.) association panel in the field with and without water deficit stress in Arizona and without water deficit stress in South Africa. Anatomical phenes displayed stress and environmental plasticity; many phenotypic responses to water deficit were adaptive, and the magnitude of response varied by genotype. We identified 57 candidate genes associated with stress and environmental plasticity and 64 candidate genes associated with phenes under well-watered and water-stress conditions in Arizona and under well-watered conditions in South Africa. Four candidate genes co-localized between plasticity groups or for phenes expressed under each condition. The genetic architecture of phenotypic plasticity is highly quantitative, and many distinct genes control plasticity in response to water deficit and different environments, which poses a challenge for breeding programs.

Maize genomes to fields (G2F): 2014-2017 field seasons: genotype, phenotype, climatic, soil, and inbred ear image datasets

OBJECTIVES

Advanced tools and resources are needed to efficiently and sustainably produce food for an increasing world population in the context of variable environmental conditions. The maize genomes to fields (G2F) initiative is a multi-institutional initiative effort that seeks to approach this challenge by developing a flexible and distributed infrastructure addressing emerging problems. G2F has generated large-scale phenotypic, genotypic, and environmental datasets using publicly available inbred lines and hybrids evaluated through a network of collaborators that are part of the G2F's genotype-by-environment (G × E) project. This report covers the public release of datasets for 2014-2017.

DATA DESCRIPTION

Datasets include inbred genotypic information; phenotypic, climatic, and soil measurements and metadata information for each testing location across years. For a subset of inbreds in 2014 and 2015, yield component phenotypes were quantified by image analysis. Data released are accompanied by README descriptions. For genotypic and phenotypic data, both raw data and a version without outliers are reported. For climatic data, a version calibrated to the nearest airport weather station and a version without outliers are reported. The 2014 and 2015 datasets are updated versions from the previously released files [1] while 2016 and 2017 datasets are newly available to the public.

Spatio-Temporal Variation in Water Uptake in Seminal and Nodal Root Systems of Barley Plants Grown in Soil

The spatial and temporal dynamics of root water uptake in nodal and seminal roots are poorly understood, especially in relation to root system development and aging. Here we non-destructively quantify 1) root water uptake and 2) root length of nodal and seminal roots of barley in three dimensions during 43 days of growth. We developed a concentric split root system to hydraulically and physically isolate the seminal and nodal root systems. Using magnetic resonance imaging (MRI), roots were visualized, root length was determined, and soil water depletion in both compartments was measured. From 19 days after germination and onwards, the nodal root system had greater water uptake compared to the seminal root system due to both greater root length and greater root conductivity. At 29 days after germination onwards, the average age of the seminal and nodal root systems was similar and no differences were observed in water uptake per root length between seminal and nodal root systems, indicating the importance of embryonic root systems for seedling establishment and nodal root systems in more mature plants. Since nodal roots perform the majority of water uptake at 29 days after germination and onwards, nodal root phenes merit consideration as a selection target to improve water capture in barley and possibly other crops.

Root cortical anatomy is associated with differential pathogenic and symbiotic fungal colonization in maize

Root anatomical phenotypes vary among maize (Zea mays) cultivars and may have adaptive value by modifying the metabolic cost of soil exploration. However, the microbial trade-offs of these phenotypes are unknown. We hypothesized that nodal roots of maize with contrasting cortical anatomy have different patterns of mutualistic and pathogenic fungal colonization. Arbuscular mycorrhizal colonization in the field and mesocosms, root rots in the field, and Fusarium verticillioides colonization in mesocosms were evaluated in maize genotypes with contrasting root cortical anatomy. Increased aerenchyma and decreased living cortical area were associated with decreased mycorrhizal colonization in mesocosm and field experiments with inbred genotypes. In contrast, mycorrhizal colonization of hybrids increased with larger aerenchyma lacunae; this increase coincided with larger root diameters of hybrid roots. F. verticillioides colonization was inversely correlated with living cortical area in mesocosm-grown inbreds, and no relation was found between root rots and living cortical area or aerenchyma in field-grown hybrids. Root rots were positively correlated with cortical cell file number and inversely correlated with cortical cell size. Mycorrhizae and root rots were inversely correlated in field-grown hybrids. We conclude that root anatomy is associated with differential effects on pathogens and mycorrhizal colonization of nodal roots in maize.

Genotypic variation and nitrogen stress effects on root anatomy in maize are node specific

Root phenotypes that improve nitrogen acquisition are avenues for crop improvement. Root anatomy affects resource capture, metabolic cost, hydraulic conductance, anchorage, and soil penetration. Cereal root phenotyping has centered on primary, seminal, and early nodal roots, yet critical nitrogen uptake occurs when the nodal root system is well developed. This study examined root anatomy across nodes in field-grown maize (Zea mays L.) hybrid and inbred lines under high and low nitrogen regimes. Genotypes with high nitrogen use efficiency (NUE) had larger root diameter and less cortical aerenchyma across nodes under stress than genotypes with lower NUE. Anatomical phenes displayed slightly hyperallometric relationships to shoot biomass. Anatomical plasticity varied across genotypes; most genotypes decreased root diameter under stress when averaged across nodes. Cortex, stele, total metaxylem vessel areas, and cortical cell file and metaxylem vessel numbers scaled strongly with root diameter across nodes. Within nodes, metaxylem vessel size and cortical cell size were correlated, and root anatomical phenotypes in the first and second nodes were not representative of subsequent nodes. Node, genotype, and nitrogen treatment affect root anatomy. Understanding nodal variation in root phenes will enable the development of plants that are adapted to low nitrogen conditions.

Laser ablation tomography for visualization of root colonization by edaphic organisms

Soil biota have important effects on crop productivity, but can be difficult to study in situ. Laser ablation tomography (LAT) is a novel method that allows for rapid, three-dimensional quantitative and qualitative analysis of root anatomy, providing new opportunities to investigate interactions between roots and edaphic organisms. LAT was used for analysis of maize roots colonized by arbuscular mycorrhizal fungi, maize roots herbivorized by western corn rootworm, barley roots parasitized by cereal cyst nematode, and common bean roots damaged by Fusarium. UV excitation of root tissues affected by edaphic organisms resulted in differential autofluorescence emission, facilitating the classification of tissues and anatomical features. Samples were spatially resolved in three dimensions, enabling quantification of the volume and distribution of fungal colonization, western corn rootworm damage, nematode feeding sites, tissue compromised by Fusarium, and as well as root anatomical phenotypes. Owing to its capability for high-throughput sample imaging, LAT serves as an excellent tool to conduct large, quantitative screens to characterize genetic control of root anatomy and interactions with edaphic organisms. Additionally, this technology improves interpretation of root-organism interactions in relatively large, opaque root segments, providing opportunities for novel research investigating the effects of root anatomical phenes on associations with edaphic organisms.

Root phenotypes for improved nutrient capture: an underexploited opportunity for global agriculture

Nutrient-efficient crops are a solution to the two grand challenges of modern agriculture: improving food security while reducing environmental impacts. The primary challenges are (1) nitrogen (N) and phosphorus (P) efficiency; (2) potassium (K), calcium (Ca), and magnesium (Mg) efficiency for acid soils; and (3) iron (Fe) and zinc (Zn) efficiency for alkaline soils. Root phenotypes are promising breeding targets for each of these. The Topsoil Foraging ideotype is beneficial for P capture and should also be useful for capture of K, Ca, and Mg in acid soils. The Steep, Cheap, and Deep ideotype for subsoil foraging is beneficial for N and water capture. Fe and Zn capture can be improved by targeting mechanisms of metal mobilization in the rhizosphere. Root hairs and phenes that reduce the metabolic cost of soil exploration should be prioritized in breeding programs. Nutrient-efficient crops should provide benefits at all input levels. Although our current understanding is sufficient to deploy root phenotypes for improved nutrient capture in crop breeding, this complex topic does not receive the resources it merits in either applied or basic plant biology. Renewed emphasis on these topics is needed in order to develop the nutrient-efficient crops urgently needed in global agriculture.