Larry M. York

Image-Based High-Throughput Field Phenotyping of Crop Roots

Automatic methods developed or reproducible field-based phenotyping allow distinction of genotypes, including 13 newly accessible plant root traits. 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.

Integration of root phenes for soil resource acquisition

Suboptimal availability of water and nutrients is a primary limitation to plant growth in terrestrial ecosystems. The acquisition of soil resources by plant roots is therefore an important component of plant fitness and agricultural productivity. Plant root systems comprise a set of phenes, or traits, that interact. Phenes are the units of the plant phenotype, and phene states represent the variation in form and function a particular phene may take. Root phenes can be classified as affecting resource acquisition or utilization, influencing acquisition through exploration or exploitation, and in being metabolically influential or neutral. These classifications determine how one phene will interact with another phene, whether through foraging mechanisms or metabolic economics. Phenes that influence one another through foraging mechanisms are likely to operate within a phene module, a group of interacting phenes, that may be co-selected. Examples of root phene interactions discussed are: (1) root hair length × root hair density, (2) lateral branching × root cortical aerenchyma (RCA), (3) adventitious root number × adventitious root respiration and basal root growth angle (BRGA), (4) nodal root number × RCA, and (5) BRGA × root hair length and density. Progress in the study of phenes and phene interactions will be facilitated by employing simulation modeling and near-isophenic lines that allow the study of specific phenes and phene combinations within a common phenotypic background. Developing a robust understanding of the phenome at the organismal level will require new lines of inquiry into how phenotypic integration influences plant function in diverse environments. A better understanding of how root phenes interact to affect soil resource acquisition will be an important tool in the breeding of crops with superior stress tolerance and reduced dependence on intensive use of inputs.

The holistic rhizosphere: integrating zones, processes, and semantics in the soil influenced by roots

Despite often being conceptualized as a thin layer of soil around roots, the rhizosphere is actually a dynamic system of interacting processes. Hiltner originally defined the rhizosphere as the soil influenced by plant roots. However, soil physicists, chemists, microbiologists, and plant physiologists have studied the rhizosphere independently, and therefore conceptualized the rhizosphere in different ways and using contrasting terminology. Rather than research-specific conceptions of the rhizosphere, the authors propose a holistic rhizosphere encapsulating the following components: microbial community gradients, macroorganisms, mucigel, volumes of soil structure modification, and depletion or accumulation zones of nutrients, water, root exudates, volatiles, and gases. These rhizosphere components are the result of dynamic processes and understanding the integration of these processes will be necessary for future contributions to rhizosphere science based upon interdisciplinary collaborations. In this review, current knowledge of the rhizosphere is synthesized using this holistic perspective with a focus on integrating traditionally separated rhizosphere studies. The temporal dynamics of rhizosphere activities will also be considered, from annual fine root turnover to diurnal fluctuations of water and nutrient uptake. The latest empirical and computational methods are discussed in the context of rhizosphere integration. Clarification of rhizosphere semantics, a holistic model of the rhizosphere, examples of integration of rhizosphere studies across disciplines, and review of the latest rhizosphere methods will empower rhizosphere scientists from different disciplines to engage in the interdisciplinary collaborations needed to break new ground in truly understanding the rhizosphere and to apply this knowledge for practical guidance.

Evolution of US maize (Zea mays L.) root architectural and anatomical phenes over the past 100 years corresponds to increased tolerance of nitrogen stress

Highlight Comprehensive analysis of maize root phenotypes over the past century indicates that they have evolved to be more efficient in acquiring nitrogen.

Intensive field phenotyping of maize (Zea mays L.) root crowns identifies phenes and phene integration associated with plant growth and nitrogen acquisition

Highlight Root phenes were phenotyped on all whorls of field-grown maize for the first time, and their integration could explain up to 70% of shoot mass variation in low nitrogen soils.

Spatiotemporal variation of nitrate uptake kinetics within the maize (Zea mays L.) root system is associated with greater nitrate uptake and interactions with architectural phenes

Highlight Nitrate uptake kinetics varied among maize root classes, and simulations demonstrated that increasing the maximum uptake rate, I max, of all roots could increase plant growth by as much as 26%.

Wheat shovelomics I: A field phenotyping approach for characterising the structure and function of root systems in tillering species

Wheat represents a major crop, yet the current rate of yield improvement is insufficient to meet its projected global food demand. Breeding root systems more efficient for water and nitrogen capture represents a promising avenue for accelerating yield gains. Root crown phenotyping, or shovelomics, relies on excavation of the upper portions of root systems in the field and measuring root properties such as numbers, angles, densities and lengths. We report a new shovelomics method that images the whole wheat root crown, then partitions it into the main shoot and tillers for more intensive phenotyping. Root crowns were phenotyped using the new method from the Rialto × Savannah population consisting of both parents and 94 doubled-haploid lines. For the whole root crown, the main shoot, and tillers, root phenes including nodal root number, growth angle, length, and diameter were measured. Substantial variation and heritability were observed for all phenes. Principal component analysis revealed latent constructs that imply pleiotropic genetic control of several related root phenes. Correlational analysis revealed that nodal root number and growth angle correlate among the whole crown, main shoot, and tillers, indicating shared genetic control among those organs. We conclude that this phenomics approach will be useful for breeding ideotype root systems in tillering species.

Wheat shovelomics II: Revealing relationships between root crown traits and crop growth

Optimization of root system architecture represents an important goal in wheat breeding. Adopting new field methods for root phenotyping is key to delivering this goal. A novel ‘shovelomics’ method was applied for phenotyping root crown traits to characterize the Savannah x Rialto doubled-haploid (DH) population in two field experiments under irrigated and rain-fed conditions. Trait validation was carried out through soil coring on a subset of 14 DH lines and the two parents. We observed that drought reduced grain yield per plant by 21.0%. Under rain-fed conditions, nodal root angle and roots shoot-1 were positively associated with root length density (RLD) at 40-60 cm depth; RLD was also positively correlated with grain yield. Nodal root angle and roots shoot-1 were also positively associated with canopy stay green and grain yield under rain-fed conditions. We conclude that shovelomics is a valuable technique for quantifying genetic variation in nodal root traits in wheat, revealing nodal root angle and root number per shoot provide useful selection criteria in breeding programs aimed at improving drought tolerance in wheat. Highlight Nodal root angle and number shoot-1 measured using ‘shovelomics’ were positively associated with root density at depth and yield under drought in a Savanah x Rialto wheat DH population.

Functional phenomics: An emerging field integrating high-throughput phenotyping, physiology, and bioinformatics

Highlight Functional phenomics is an emerging field in plant biology that relies on high-throughput phenotyping, big data analytics, controlled manipulative experiments, and simulation modelling to increase understanding of plant physiology. Abstract The emergence of functional phenomics signifies the rebirth of physiology as a 21st century science through the use of advanced sensing technologies and big data analytics. Functional phenomics highlights the importance of phenotyping beyond only identifying genetic regions because a significant knowledge gap remains in understanding which plant properties will influence ecosystem services beneficial to human welfare. Here, a general approach for the theory and practice of functional phenomics is outlined including exploring the phene concept as a unit of phenotype. The functional phenomics pipeline is proposed as a general method for conceptualizing, measuring, and validating utility of plant phenes. The functional phenomics pipeline begins with ideotype development. Second, a phenotyping platform is developed to maximize the throughput of phene measurements. A mapping population is screened measuring target phenes and indicators of plant performance such as yield and nutrient composition. Traditional forward genetics allows genetic mapping, while functional phenomics links phenes to plant performance. Based on these data, genotypes with contrasting phenotypes can be selected for smaller yet more intensive experiments to understand phene-environment interactions in depth. Simulation modeling can be used to understand the phenotypes and all stages of the pipeline feed back to ideotype and phenotyping platform development.

A novel multi-perspective imaging platform (M-PIP) for phenotyping soybean root crowns in the field increases throughput and separation ability of genotype root properties

Background Root crown phenotyping has linked root properties to shoot mass, nutrient uptake, and yield in the field, which increases the understanding of soil resource acquisition and presents opportunities for breeding. The original methods using manual measurements have been largely supplanted by image-based approaches. However, most image-based systems have been limited to one or two perspectives and rely on segmentation from grayscale images. An efficient high-throughput root crown phenotyping system is introduced that takes images from five perspectives simultaneously, constituting the Multi-Perspective Imaging Platform (M-PIP). A segmentation procedure using the Expectation-Maximization Gaussian Mixture Model (EM-GMM) algorithm was developed to distinguish plant root pixels from background pixels in color images and using hardware acceleration (CPU and GPU). Phenes were extracted using MatLab scripts. Placement of excavated root crowns for image acquisition was standardized and is ergonomic. The M-PIP was tested on 24 soybean [Glycine max (L.) Merr.] cultivars released between 1930 and 2005. Results Relative to previous reports of imaging throughput, this system provides greater throughput with sustained rates of 1.66 root crowns min-1. The EM-GMM segmentation algorithm with hardware acceleration was able to segment images in 10 s, faster than previous methods, and the output images were consistently better connected with less loss of fine detail. Image-based phenes had similar heritabilities as manual measures with the greatest effect sizes observed for Maximum Radius and Fine Radius Frequency. Correlations were also noted, especially among the manual Complexity score and phenes such as number of roots and Total Root Length. Averaging phenes across perspectives generally increased heritability, and no single perspective consistently performed better than others. Angle-based phenes, Fineness Index, Maximum Width, Holes, Solidity and Width-to-Depth Ratio were the most sensitive to perspective with decreased correlations among perspectives. Conclusion The substantial heritabilities measured for many phenes suggest that they are potentially useful for breeding. Multiple perspectives together often produced the greatest heritabilities, and no single perspective consistently performed better than others. Thus, as illustrated here for soybean, multiple perspectives may be beneficial for root crown phenotyping systems. This system can contribute to breeding efforts that incorporate under-utilized root phenotypes to increase food security and sustainability.