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Purpose Commonly, root length distributions are used as a first approximation of root water uptake profiles. In this study we want to test the underlying hypothesis of a constant water uptake rate per unit root length over depth. Methods Root water uptake profiles were measured using a novel sensor technology. Root length was measured with MRI and by scanning harvested roots. Experiments were performed with pot-grown barley (Hordeum vulgare), maize (Zea mays), faba bean (Vicia faba), and zucchini (Cucurbita pepo). Results For barley, maize, and faba bean, we found that roots in the top 15 cm had significantly greater water uptake rates per unit length than roots in the bottom 30 cm. For zucchini, the trend was similar but not significant. Therefore, variation of root water uptake rates with depth could be explained only partly (61–71%) by a variation of root length with depth. Conclusion The common approximation of root water uptake profiles by root length distributions relies on constant water uptake rates per unit root length. This hypothesis does not hold in our study, as we found significantly greater water uptake rates per unit length in shallower than in deeper roots. This trend was consistent among species, despite the partly strong variation in physiological parameters. We suggest that this is caused by a decreasing axial transport conductance with depth. This might result in a general underestimation of water uptake rates in shallow soil layers when they are approximated by the root length distribution.

Plant and soil (2022). doi:10.1007/s11104-021-05201-7, Published by Springer Science + Business Media B.V, Dordrecht [u.a.]

Moderate soil drying can cause a strong decrease in the soil-root system conductance. The resulting impact on root water uptake depends on the spatial distribution of the altered conductance relatively to remaining soil water resources, which is largely unknown. Here, we analyzed the vertical distribution of conductance across root systems using a novel, noninvasive sensor technology on pot-grown faba bean and maize plants. Withholding water for 4 days strongly enhanced the vertical gradient in soil water potential. Therefore, roots in upper and deeper soil layers were affected differently: In drier, upper layers, root conductance decreased by 66%–72%, causing an amplification of the drop in leaf water potential. In wetter, deeper layers, root conductance increased in maize but not in faba bean. The consequently facilitated deep-water uptake in maize contributed up to 21% of total water uptake at the end of the measurement. Analysis of root length distributions with MRI indicated that the locally increased conductance was mainly caused by an increased intrinsic conductivity and not by additional root growth. Our findings show that plants can partly compensate for a reduced root conductance in upper, drier soil layers by locally increasing root conductivity in wetter layers, thereby improving deep-water uptake.

Functional-structural plant models are valuable modeling tools in analyzing plant development. A functional-structural plant model combines a three-dimensional representation of plant structure with models for physiological functions in order to better understand plant development. We present a guide to simulating crop root systems with OpenSimRoot, a feature-rich, highly cited, and open-source functional-structural root architecture model. We describe in detail how to create your own input files in conjunction with some examples. The aim of this guide is to highlight the potential of computational modeling in biology and to make modeling more accessible to the plant science community.

Functional-structural plant models are valuable modeling tools in analyzing plant development. A functional-structural plant model combines a three-dimensional representation of plant structure with models for physiological functions in order to better understand plant development. We present a guide to simulating crop root systems with OpenSimRoot, a feature-rich, highly cited, and open-source functional-structural root architecture model. We describe in detail how to create your own input files in conjunction with some examples. The aim of this guide is to highlight the potential of computational modeling in biology and to make modeling more accessible to the plant science community.

Background While clonal integration can improve the performance of rhizomatous plants, it remains unclear whether their clonal integration strategy changes under contrasting clipping and saline-alkali homogeneous and heterogeneous environments. Leymus chinensis is a clonal grass native to the Songnen grassland where heavy grazing and patchy saline-alkali stress are serious environmental and ecological problems. We hypothesized that L. chinensis overcomes these stresses through clonal integration, in particular the transfer of nitrogen and carbohydrates. Methods A pot experiment was carried out with 15N isotope soil labeling method to study clonal integration strategy in the connected mother and daughter ramets of L. chinensis. The connected ramet pairs were grown in homogeneous (both connected ramets were treated) and heterogeneous (only daughter ramets were treated) environments with four treatments: control, clipping (60% aboveground biomass removal), saline-alkali (3.45 g of NaCl, NaHCO3, and Na2CO3 per pot), and clipping × saline-alkali. Results A significant amount (22.5%) of 15N was transferred from mother to daughter ramets under non-stressed conditions. When homogeneously stressing both mother and daughter ramets, N transfer was significantly reduced to 8.5‐–14.6%, independent of the nature of the stress. When only daughters were stressed (heterogeneous stress), saline-alkali stress led to a division of labor where daughters had enhanced photosynthesis, and mother ramets had increased 15N uptake and growth. Clipping only daughters reduced biomass and 15N uptake of both daughter and mother ramets. Conclusions Our results demonstrated that clonal integration also occurs in homogeneous favorable environments but is reduced under homogeneous stress. In heterogeneous environments, clonal integration is used to translocate resource after clipping and a division of labor is established to overcome saline-alkali stress. Clonal integration continued even when daughters were severely stressed by the combined treatments. Our findings suggest that these mechanisms are key to the success of L. chinensis in the Songnen grassland.

Root architectural (RSA) models have become important tools in root research and plant phenotyping for studying root traits, processes, and interactions with the environment. The models have been used to simulate how various root traits and processes influence water and nutrient uptake. At a more technical level, they have been used to develop phenotyping technology, particularly for testing algorithms for segmenting roots. To compute these quantitative estimates regarding plant nutrition and root functioning, much development occurred in the last decade increasing the complexity of the models. This chapter describes first the application of the models to questions in plant biology, breeding, and agronomy, and second the development of the models. It concludes with a small outlook suggesting that models need benchmarking and validation and that new developments are likely to include better descriptions of root plasticity responses and focus on biological interactions among (soil) organisms, including mycorrhizal fungi.

International audience; The effects of plants on the biosphere, atmosphere and geosphere are key determinants of terrestrial ecosystem functioning. However, despite substantial progress made regarding plant belowground components, we are still only beginning to explore the complex relationships between root traits and functions. Drawing on the literature in plant physiology, ecophysiology, ecology, agronomy and soil science, we reviewed 24 aspects of plant and ecosystem functioning and their relationships with a number of root system traits, including aspects of architecture, physiology, morphology, anatomy, chemistry, biomechanics and biotic interactions. Based on this assessment, we critically evaluated the current strengths and gaps in our knowledge, and identify future research challenges in the field of root ecology. Most importantly, we found that belowground traits with the broadest importance in plant and ecosystem functioning are not those most commonly measured. Also, the estimation of trait relative importance for functioning requires us to consider a more comprehensive range of functionally relevant traits from a diverse range of species, across environments and over time series. We also advocate that establishing causal hierarchical links among root traits will provide a hypothesis-based framework to identify the most parsimonious sets of traits with the strongest links on functions, and to link genotypes to plant and ecosystem functioning.

In the context of a recent massive increase in research on plant root functions and their impact on the environment, root ecologists currently face many important challenges to keep on generating cutting-edge, meaningful and integrated knowledge. Consideration of the below-ground components in plant and ecosystem studies has been consistently called for in recent decades, but methodology is disparate and sometimes inappropriate. This handbook, based on the collective effort of a large team of experts, will improve trait comparisons across studies and integration of information across databases by providing standardised methods and controlled vocabularies. It is meant to be used not only as starting point by students and scientists who desire working on below-ground ecosystems, but also by experts for consolidating and broadening their views on multiple aspects of root ecology. Beyond the classical compilation of measurement protocols, we have synthesised recommendations from the literature to provide key background knowledge useful for: (1) defining below-ground plant entities and giving keys for their meaningful dissection, classification and naming beyond the classical fine-root vs coarse-root approach; (2) considering the specificity of root research to produce sound laboratory and field data; (3) describing typical, but overlooked steps for studying roots (e.g. root handling, cleaning and storage); and (4) gathering metadata necessary for the interpretation of results and their reuse. Most importantly, all root traits have been introduced with some degree of ecological context that will be a foundation for understanding their ecological meaning, their typical use and uncertainties, and some methodological and conceptual perspectives for future research. Considering all of this, we urge readers not to solely extract protocol recommendations for trait measurements from this work, but to take a moment to read and reflect on the extensive information contained in this broader guide to root ecology, including sections I–VII and the many introductions to each section and root trait description. Finally, it is critical to understand that a major aim of this guide is to help break down barriers between the many subdisciplines of root ecology and ecophysiology, broaden researchers’ views on the multiple aspects of root study and create favourable conditions for the inception of comprehensive experiments on the role of roots in plant and ecosystem functioning. ISSN:0028-646X ISSN:1469-8137

Cereal plant density is a relevant agronomic trait in agriculture and high-throughput phenotyping of plant density is important for the decision-making process in precision farming and breeding. It influences the water as well as the fertilization requirements, the intraspecific competition, and the occurrence of weeds or pathogens. Recent studies have determined plant density using machine-learning approaches and feature extraction. This requires spatially very highly resolved images (0.02 cm) because the accuracy distinctly decreased when images had lower resolution. In this study, we present an approach that uses the linear relationship between plant density manually counted in the field and fractional cover derived from a RGB and a multispectral camera equipped on an unmanned aerial vehicle (UAV). We assumed that at an early seedling stage fractional cover is closely related to the number of plants. Spring barley and spring wheat experiments, each with three genotypes and four different sowing densities, were examined. The practicability and repeatability of the methodology were evaluated with an independent experiment consisting of 42 winter wheat genotypes. This experiment mainly differed for genotypes, sowing density and season. The empirical regression models that make us of multispectral images having a GSD of 0.69 cm were able to determine plant density with a high prediction accuracy for barley and wheat (R2 > 0.91, mean absolute error (MAE) The method and the use of UAV image data enable high-throughput phenotyping of cereal plant density with uncertainties of less than 10 %.The practicability, repeatability and robustness of the developed approach were demonstrated in this study.

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.

Three-dimensional models of root growth, architecture and function are becoming important tools that aid the design of agricultural management schemes and the selection of beneficial root traits. However, while benchmarking is common in many disciplines that use numerical models, such as natural and engineering sciences, functional-structural root architecture models have never been systematically compared. The following reasons might induce disagreement between the simulation results of different models: different representation of root growth, sink term of root water and solute uptake and representation of the rhizosphere. Presently, the extent of discrepancies is unknown, and a framework for quantitatively comparing functional-structural root architecture models is required. We propose, in a first step, to define benchmarking scenarios that test individual components of complex models: root architecture, water flow in soil and water flow in roots. While the latter two will focus mainly on comparing numerical aspects, the root architectural models have to be compared at a conceptual level as they generally differ in process representation. Therefore, defining common inputs that allow recreating reference root systems in all models will be a key challenge. In a second step, benchmarking scenarios for the coupled problems are defined. We expect that the results of step 1 will enable us to better interpret differences found in step 2. This benchmarking will result in a better understanding of the different models and contribute toward improving them. Improved models will allow us to simulate various scenarios with greater confidence and avoid bugs, numerical errors or conceptual misunderstandings. This work will set a standard for future model development. ispartof: FRONTIERS IN PLANT SCIENCE vol:11 ispartof: location:Switzerland status: published

Root systems determine the water and nutrients for photosynthesis and harvested products, underpinning agricultural productivity. We highlight 11 programs that integrated root traits into germplasm for breeding, relying on phenotyping. Progress was successful but slow. Today's phenotyping technologies will speed up root trait improvement. They combine multiple new alleles in germplasm for target environments, in parallel. Roots and shoots are detected simultaneously and nondestructively, seed to seed measures are automated, and field and laboratory technologies are increasingly linked. Available simulation models can aid all phenotyping decisions. This century will see a shift from single root traits to rhizosphere selections that can be managed dynamically on farms and a shift to phenotype-based improvement to accommodate the dynamic complexity of whole crop systems.

• Background and Aims Root cortical senescence (RCS) is a poorly understood phenomenon with implications for adaptation to edaphic stress. It was hypothesized that RCS in barley (Hordeum vulgare L.) is (1) accelerated by exogenous ethylene exposure; (2) accompanied by differential expression of ethylene synthesis and signalling genes; and (3) associated with differential expression of programmed cell death (PCD) genes. • Methods Gene expression of root segments from four barley genotypes with and without RCS was evaluated using quantitative real-time PCR (qRT-PCR). The progression of RCS was manipulated with root zone ethylene and ethylene inhibitor applications. • Key Results The results demonstrate that ethylene modulates RCS. Four genes related to ethylene synthesis and signalling were upregulated during RCS in optimal, low nitrogen and low phosphorus nutrient regimes. RCS was accelerated by root zone ethylene treatment, and this effect was reversed by an ethylene action inhibitor. Roots treated with exogenous ethylene had 35 and 46 % more cortical senescence compared with the control aeration treatment in seminal and nodal roots, respectively. RCS was correlated with expression of two genes related to programmed cell death (PCD). • Conclusions The development of RCS is similar to root cortical aerenchyma formation with respect to ethylene modulation of the PCD process.

The 1D radial solute transport model with non-linear inner boundary condition is widely used for simulating nutrient uptake by plant roots. When included into an architectural root model, this local model has to be solved for a high number of root segments, e. g. 10 5 – 10 6 segments for large root systems. Each root segment comes with its own local parameter set in heterogeneous root architectural models. Depending on the soil and solute, the effective diffusion coefficient spans over more than six orders (e. g. for N, K, and P). Thus a numerical implementation of this rhizosphere transport model is required to be fast, accurate and stable for a large parameter space. We apply 13 methods to this rhizosphere model with root hairs and compare their accuracy, computational speed, and applicability. In particular, the Crank-Nicolson method is compared to higher-order explicit adaptive methods and some stiff solvers. The Crank-Nicolson method sometimes oscillated and was up to a hundred times slower than an explicit adaptive scheme with similar accuracy. For a given spatial resolution, Crank-Nicolson had about one order lower accuracy as other tested methods. The maximum spatial time step can be estimated from root radius, solute diffusion, advection, and soil buffer power. Although Crank-Nicolson is a viable method and often used as de-facto standard method for rhizosphere models, it was not the best performer in our comparison. While the best method remains problem specific, for general use in root architectural models we recommend adaptive Runge-Kutta with cubic or quadratic upwind for advection.

The functional implications of root cortical senescence (RCS) are poorly understood. We tested the hypotheses that RCS in barley (1) reduces the respiration and nutrient content of root tissue; (2) decreases radial water and nutrient transport; and (3) is accompanied by increased suberization to protect the stele. Genetic variation for RCS exists between modern germplasm and landraces. Nitrogen and phosphorus deficiency increased the rate of RCS. Maximal RCS, defined as the disappearance of the entire root cortex, reduced root nitrogen content by 66%, phosphorus content by 63% and respiration by 87% compared with root segments with no RCS. Roots with maximal RCS had 90, 92 and 84% less radial water, nitrate and phosphorus transport, respectively, compared with segments with no RCS. The onset of RCS coincided with 30% greater aliphatic suberin in the endodermis. These results support the hypothesis that RCS reduces root carbon and nutrient costs and may therefore have adaptive significance for soil resource acquisition. By reducing root respiration and nutrient content, RCS could permit greater root growth, soil resource acquisition and resource allocation to other plant processes. RCS merits investigation as a trait for improving the performance of barley, wheat, triticale and rye under edaphic stress.

AimPreviously, we showed that sowing density influences root length density (RLD), specific root length (SRL) especially in the topsoil, and shallowness of fine roots of field grown spring barley (Hordeum vulagre L.). Here, we ask which trait components may explain these observed changes.MethodWe grew two spring barley cultivars at contrasting sowing densities in both field trials and rhizotrons, and excavated root crowns and imaged root growth.ResultsIn the field, tiller and nodal root numbers per plant decreased with increasing sowing density, however, nodal roots per tiller, seminal roots per plant, and lateral branching frequencies were not affected. Branching angle did not or only slightly declined with increasing sowing density. In rhizotrons, aboveground only tiller number was affected by sowing density. Root growth rates and counts were not (or only slightly) affected.ConclusionGreater RLD at high sowing densities is largely explained by greater main root number per area. The altered seminal to nodal root ratio might explain observed increases in SRL. We conclude that sowing density is a modifier of root system architecture with probable functional consequences, and thereby an important factor to be considered in root studies or the development of root ideotypes for agriculture. AimPreviously, we showed that sowing density influences root length density (RLD), specific root length (SRL) especially in the topsoil, and shallowness of fine roots of field grown spring barley (Hordeum vulagre L.). Here, we ask which trait components may explain these observed changes.MethodWe grew two spring barley cultivars at contrasting sowing densities in both field trials and rhizotrons, and excavated root crowns and imaged root growth.ResultsIn the field, tiller and nodal root numbers per plant decreased with increasing sowing density, however, nodal roots per tiller, seminal roots per plant, and lateral branching frequencies were not affected. Branching angle did not or only slightly declined with increasing sowing density. In rhizotrons, aboveground only tiller number was affected by sowing density. Root growth rates and counts were not (or only slightly) affected.ConclusionGreater RLD at high sowing densities is largely explained by greater main root number per area. The altered seminal to nodal root ratio might explain observed increases in SRL. We conclude that sowing density is a modifier of root system architecture with probable functional consequences, and thereby an important factor to be considered in root studies or the development of root ideotypes for agriculture.

We present examples of root plasticity in response to soil compaction and discuss their role in plant performance. Subsequently, we propose a plastic ideotype for soil compaction tolerance., Soil compaction is a serious global problem, and is a major cause of inadequate rooting and poor yield in crops around the world. Root system architecture (RSA) describes the spatial arrangement of root components within the soil and determines the plant’s exploration of the soil. Soil strength restricts root growth and may slow down root system development. RSA plasticity may have an adaptive value, providing environmental tolerance to soil compaction. However, it is challenging to distinguish developmental retardation (apparent plasticity) or responses to severe stress from those root architectural changes that may provide an actual environmental tolerance (adaptive plasticity). In this review, we outline the consequences of soil compaction on the rooting environment and extensively review the various root responses reported in the literature. Finally, we discuss which responses enhance root exploration capabilities in tolerant genotypes, and to what extent these responses might be useful for breeding. We conclude that RSA plasticity in response to soil compaction is complex and can be targeted in breeding to increase the performance of crops under specific agronomical conditions.

Background and aims Root architecture is a primary determinant of soil resource acquisition. We hypothesized that root architectural phenes will display both positive and negative interactions with each other for soil resource capture because of competition for internal resources and functional trade-offs in soil exploration. Methods We employed the functional-structural plant model SimRoot to explore how interactions among architectural phenes in common bean determine the acquisition of phosphate and nitrate, two key soil resources contrasting in mobility. We evaluated the utility of basal root whorl number (BRWN) when basal root growth angle, hypocotyl-borne roots and lateral root branching density (LRBD) were varied, under varying availability of phosphate and nitrate. Key results Three basal root whorls were optimal in most phenotypes. This optimum shifted towards greater values when LRBD decreased and to smaller numbers when LRBD increased. The maximum biomass accumulated for a given BRWN phenotype in a given limiting nutrient scenario depended upon root growth angle. Under phosphorus stress shallow phenotypes grew best, whereas under nitrate stress fanned phenotypes grew best. The effect of increased hypocotyl-borne roots depended upon BRWN as well as the limiting nutrient. Greater production of axial roots due to BRWN or hypocotyl-borne roots reduced rooting depth, leading to reduced biomass under nitrate-limiting conditions. Increased BRWN as well as greater LRBD increased root carbon consumption, resulting in reduced shoot biomass. Conclusions We conclude that the utility of a root architectural phenotype is determined by whether the constituent phenes are synergistic or antagonistic. Competition for internal resources and trade-offs for external resources result in multiple phenotypes being optimal under a given nutrient regime. We also find that no single phenotype is optimal across contrasting environments. These results have implications for understanding plant evolution and also for the breeding of more stress-tolerant crop phenotypes.

Root cortical senescence (RCS) in Triticeae reduces nutrient uptake, nutrient content, respiration, and radial hydraulic conductance of root tissue. We used the functional-structural model SimRoot to evaluate the functional implications of RCS in barley (Hordeum vulgare) under suboptimal nitrate, phosphorus, and potassium availability. The utility of RCS was evaluated using sensitivity analyses in contrasting nutrient regimes. At flowering (80 d), RCS increased simulated plant growth by up to 52%, 73%, and 41% in nitrate-, phosphorus-, and potassium-limiting conditions, respectively. Plants with RCS had reduced nutrient requirement of root tissue for optimal plant growth, reduced total cumulative cortical respiration, and increased total carbon reserves. Nutrient reallocation during RCS had a greater effect on simulated plant growth than reduced respiration or nutrient uptake. Under low nutrient availability, RCS had greater benefit in plants with fewer tillers. RCS had greater benefit in phenotypes with fewer lateral roots at low nitrate availability, but the opposite was true in low phosphorus or potassium availability. Additionally, RCS was quantified in field-grown barley in different nitrogen regimes. Field and virtual soil coring simulation results demonstrated that living cortical volume per root length (an indicator of RCS) decreased with depth in younger plants, while roots of older plants had very little living cortical volume per root length. RCS may be an adaptive trait for nutrient acquisition by reallocating nutrients from senescing tissue and secondarily by reducing root respiration. These simulated results suggest that RCS merits investigation as a breeding target for enhanced soil resource acquisition and edaphic stress tolerance.

Since ancient times in the Americas, maize, bean and squash have been grown together in a polyculture known as the 'three sisters'. This polyculture and its maize/bean variant have greater yield than component monocultures on a land-equivalent basis. This study shows that below-ground niche complementarity may contribute to this yield advantage.Monocultures and polycultures of maize, bean and squash were grown in two seasons in field plots differing in nitrogen (N) and phosphorus (P) availability. Root growth patterns of individual crops and entire polycultures were determined using a modified DNA-based technique to discriminate roots of different species.The maize/bean/squash and maize/bean polycultures had greater yield and biomass production on a land-equivalent basis than the monocultures. Increased biomass production was largely caused by a complementarity effect rather than a selection effect. The differences in root crown architecture and vertical root distribution among the components of the 'three sisters' suggest that these species have different, possibly complementary, nutrient foraging strategies. Maize foraged relatively shallower, common bean explored the vertical soil profile more equally, while the root placement of squash depended on P availability. The density of lateral root branching was significantly greater for all species in the polycultures than in the monocultures.It is concluded that species differences in root foraging strategies increase total soil exploration, with consequent positive effects on the growth and yield of these ancient polycultures.

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. 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. 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. 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 functional implications of root cortical senescence (RCS) are poorly understood. We tested the hypotheses that RCS in barley (1) reduces the respiration and nutrient content of root tissue; (2) decreases radial water and nutrient transport; and (3) is accompanied by increased suberization to protect the stele. Genetic variation for RCS exists between modern germplasm and landraces. Nitrogen and phosphorus deficiency increased the rate of RCS. Maximal RCS, defined as the disappearance of the entire root cortex, reduced root nitrogen content by 66%, phosphorus content by 63% and respiration by 87% compared with root segments with no RCS. Roots with maximal RCS had 90, 92 and 84% less radial water, nitrate and phosphorus transport, respectively, compared with segments with no RCS. The onset of RCS coincided with 30% greater aliphatic suberin in the endodermis. These results support the hypothesis that RCS reduces root carbon and nutrient costs and may therefore have adaptive significance for soil resource acquisition. By reducing root respiration and nutrient content, RCS could permit greater root growth, soil resource acquisition and resource allocation to other plant processes. RCS merits investigation as a trait for improving the performance of barley, wheat, triticale and rye under edaphic stress.

BACKGROUNDS AND AIMS Crops with reduced requirement for nitrogen (N) fertilizer would have substantial benefits in developed nations, while improving food security in developing nations. This study employs the functional structural plant model SimRoot to test the hypothesis that variation in the growth angles of axial roots of maize (Zea mays L.) is an important determinant of N capture. METHODS Six phenotypes contrasting in axial root growth angles were modelled for 42 d at seven soil nitrate levels from 10 to 250 kg ha(-1) in a sand and a silt loam, and five precipitation regimes ranging from 0·5× to 1·5× of an ambient rainfall pattern. Model results were compared with soil N measurements of field sites with silt loam and loamy sand textures. KEY RESULTS For optimal nitrate uptake, root foraging must coincide with nitrate availability in the soil profile, which depends on soil type and precipitation regime. The benefit of specific root architectures for efficient N uptake increases with decreasing soil N content, while the effect of soil type increases with increasing soil N level. Extreme root architectures are beneficial under extreme environmental conditions. Extremely shallow root systems perform well under reduced precipitation, but perform poorly with ambient and greater precipitation. Dimorphic phenotypes with normal or shallow seminal and very steep nodal roots performed well in all scenarios, and consistently outperformed the steep phenotypes. Nitrate uptake increased under reduced leaching conditions in the silt loam and with low precipitation. CONCLUSIONS Results support the hypothesis that root growth angles are primary determinants of N acquisition in maize. With decreasing soil N status, optimal angles resulted in 15-50 % greater N acquisition over 42 d. Optimal root phenotypes for N capture varied with soil and precipitation regimes, suggesting that genetic selection for root phenotypes could be tailored to specific environments.

'Summary' I. 'Introduction' 839 II. 'Phenotypic differences between lab- and field-grown plants' 839 III. 'The shoot environment' 841 IV. 'The root environment' 845 V. 'Effects of plant density' 847 VI. 'Consistency among species or genotypes in ranking between lab and field' 848 VII. 'Translation of lab results to the field' 849 VIII. 'Conclusions' 851 'Acknowledgements' 851 'Author contributions' 852 References 852 Appendix A1 854 Summary Plant biologists often grow plants in growth chambers or glasshouses with the ultimate aim to understand or improve plant performance in the field. What is often overlooked is how results from controlled conditions translate back to field situations. A meta-analysis showed that lab-grown plants had faster growth rates, higher nitrogen concentrations and different morphology. They remained smaller, however, because the lab plants had grown for a much shorter time. We compared glasshouse and growth chamber conditions with those in the field and found that the ratio between the daily amount of light and daily temperature (photothermal ratio) was consistently lower under controlled conditions. This may strongly affect a plant's source : sink ratio and hence its overall morphology and physiology. Plants in the field also grow at higher plant densities. A second meta-analysis showed that a doubling in density leads on average to 34% smaller plants with strong negative effects on tiller or side-shoot formation but little effect on plant height. We found the r2 between lab and field phenotypic data to be rather modest (0.26). Based on these insights, we discuss various alternatives to facilitate the translation from lab results to the field, including several options to apply growth regimes closer to field conditions.

BACKGROUND AND AIMS During their domestication, maize, bean and squash evolved in polycultures grown by small-scale farmers in the Americas. Polycultures often overyield on low-fertility soils, which are a primary production constraint in low-input agriculture. We hypothesized that root architectural differences among these crops causes niche complementarity and thereby greater nutrient acquisition than corresponding monocultures. METHODS A functional-structural plant model, SimRoot, was used to simulate the first 40 d of growth of these crops in monoculture and polyculture and to determine the effects of root competition on nutrient uptake and biomass production of each plant on low-nitrogen, -phosphorus and -potassium soils. KEY RESULTS Squash, the earliest domesticated crop, was most sensitive to low soil fertility, while bean, the most recently domesticated crop, was least sensitive to low soil fertility. Nitrate uptake and biomass production were up to 7 % greater in the polycultures than in the monocultures, but only when root architecture was taken into account. Enhanced nitrogen capture in polycultures was independent of nitrogen fixation by bean. Root competition had negligible effects on phosphorus or potassium uptake or biomass production. CONCLUSIONS We conclude that spatial niche differentiation caused by differences in root architecture allows polycultures to overyield when plants are competing for mobile soil resources. However, direct competition for immobile resources might be negligible in agricultural systems. Interspecies root spacing may also be too large to allow maize to benefit from root exudates of bean or squash. Above-ground competition for light, however, may have strong feedbacks on root foraging for immobile nutrients, which may increase cereal growth more than it will decrease the growth of the other crops. We note that the order of domestication of crops correlates with increasing nutrient efficiency, rather than production potential.

Background and aims Root plasticity in response to the edaphic environment represents a challenge in the quantification of phenotypic variation in crop germplasm. The aim of this study was to use various growth systems to assess phenotypic variation among wild genotypes of Lupinus angustifolius.

Root cortical aerenchyma (RCA) is induced by hypoxia, drought, and several nutrient deficiencies. Previous research showed that RCA formation reduces the respiration and nutrient content of root tissue. We used SimRoot, a functional-structural model, to provide quantitative support for the hypothesis that RCA formation is a useful adaptation to suboptimal availability of phosphorus, nitrogen, and potassium by reducing the metabolic costs of soil exploration in maize (Zea mays). RCA increased the growth of simulated 40-d-old maize plants up to 55%, 54%, or 72% on low nitrogen, phosphorus, or potassium soil, respectively, and reduced critical fertility levels by 13%, 12%, or 7%, respectively. The greater utility of RCA on low-potassium soils is associated with the fact that root growth in potassium-deficient plants was more carbon limited than in phosphorus- and nitrogen-deficient plants. In contrast to potassium-deficient plants, phosphorus- and nitrogen-deficient plants allocate more carbon to the root system as the deficiency develops. The utility of RCA also depended on other root phenes and environmental factors. On low-phosphorus soils (7.5 μm), the utility of RCA was 2.9 times greater in plants with increased lateral branching density than in plants with normal branching. On low-nitrate soils, the utility of RCA formation was 56% greater in coarser soils with high nitrate leaching. Large genetic variation in RCA formation and the utility of RCA for a range of stresses position RCA as an interesting crop-breeding target for enhanced soil resource acquisition.

Background and aims The formation of root cortical aerenchyma (RCA) reduces root respiration and nutrient content by converting living tissue to air volume. It was hypothesized that RCA increases soil resource acquisition by reducing the metabolic and phosphorus cost of soil exploration. Methods To test the quantitative logic of the hypothesis, SimRoot, a functional-structural plant model with emphasis on root architecture and nutrient acquisition, was employed. Sensitivity analyses for the effects of RCA on the initial 40 d of growth of maize (Zea mays) and common bean (Phaseolus vulgaris) were conducted in soils with varying degrees of phosphorus availability. With reference to future climates, the benefit of having RCA in high CO(2) environments was simulated. Key results The model shows that RCA may increase the growth of plants faced with suboptimal phosphorus availability up to 70 % for maize and 14 % for bean after 40 d of growth. Maximum increases were obtained at low phosphorus availability (3 µm). Remobilization of phosphorus from dying cells had a larger effect on plant growth than reduced root respiration. The benefit of both these functions was additive and increased over time. Larger benefits may be expected for mature plants. Sensitivity analysis for light-use efficiency showed that the benefit of having RCA is relatively stable, suggesting that elevated CO(2) in future climates will not significantly effect the benefits of having RCA. Conclusions The results support the hypothesis that RCA is an adaptive trait for phosphorus acquisition by remobilizing phosphorus from the root cortex and reducing the metabolic costs of soil exploration. The benefit of having RCA in low-phosphorus soils is larger for maize than for bean, as maize is more sensitive to low phosphorus availability while it has a more 'expensive' root system. Genetic variation in RCA may be useful for breeding phosphorus-efficient crop cultivars, which is important for improving global food security.

Shallow basal root growth angle (BRGA) increases phosphorus acquisition efficiency by enhancing topsoil foraging because in most soils, phosphorus is concentrated in the topsoil. Root hair length and density (RHL/D) increase phosphorus acquisition by expanding the soil volume subject to phosphorus depletion through diffusion. We hypothesized that shallow BRGA and large RHL/D are synergetic for phosphorus acquisition, meaning that their combined effect is greater than the sum of their individual effects. To evaluate this hypothesis, phosphorus acquisition in the field in Mozambique was compared among recombinant inbred lines of common bean (Phaseolus vulgaris) having four distinct root phenotypes: long root hairs and shallow basal roots, long root hairs and deep basal roots, short root hairs and shallow basal roots, and short root hairs and deep basal roots. The results revealed substantial synergism between BRGA and RHL/D. Compared with short-haired, deep-rooted phenotypes, long root hairs increased shoot biomass under phosphorus stress by 89%, while shallow roots increased shoot biomass by 58%. Genotypes with both long root hairs and shallow roots had 298% greater biomass accumulation than short-haired, deep-rooted phenotypes. Therefore, the utility of shallow basal roots and long root hairs for phosphorus acquisition in combination is twice as large as their additive effects. We conclude that the anatomical phene of long, dense root hairs and the architectural phene of shallower basal root growth are synergetic for phosphorus acquisition. Phene synergism may be common in plant biology and can have substantial importance for plant fitness, as shown here.

Observed phenotypic variation in the lateral root branching density (LRBD) in maize (Zea mays) is large (1-41 cm(-1) major axis [i.e. brace, crown, seminal, and primary roots]), suggesting that LRBD has varying utility and tradeoffs in specific environments. Using the functional-structural plant model SimRoot, we simulated the three-dimensional development of maize root architectures with varying LRBD and quantified nitrate and phosphorus uptake, root competition, and whole-plant carbon balances in soils varying in the availability of these nutrients. Sparsely spaced (less than 7 branches cm(-1)), long laterals were optimal for nitrate acquisition, while densely spaced (more than 9 branches cm(-1)), short laterals were optimal for phosphorus acquisition. The nitrate results are mostly explained by the strong competition between lateral roots for nitrate, which causes increasing LRBD to decrease the uptake per unit root length, while the carbon budgets of the plant do not permit greater total root length (i.e. individual roots in the high-LRBD plants stay shorter). Competition and carbon limitations for growth play less of a role for phosphorus uptake, and consequently increasing LRBD results in greater root length and uptake. We conclude that the optimal LRBD depends on the relative availability of nitrate (a mobile soil resource) and phosphorus (an immobile soil resource) and is greater in environments with greater carbon fixation. The median LRBD reported in several field screens was 6 branches cm(-1), suggesting that most genotypes have an LRBD that balances the acquisition of both nutrients. LRBD merits additional investigation as a potential breeding target for greater nutrient acquisition.

In recent years the study of root phenotypic plasticity in response to sub-optimal environmental factors and the genetic control of these responses have received renewed attention. As a path to increased productivity, in particular for low fertility soils, several applied research projects worldwide target the improvement of crop root traits both in plant breeding and biotechnology contexts. To assist these tasks and address the challenge of optimizing root growth and architecture for enhanced mineral resource use, the development of realistic simulation models is of great importance. We review this research field from a modeling perspective focusing particularly on nutrient acquisition strategies for crop production on low nitrogen and low phosphorous soils. Soil heterogeneity and the dynamics of nutrient availability in the soil pose a challenging environment in which plants have to forage efficiently for nutrients in order to maintain their internal nutrient homeostasis throughout their life cycle. Mathematical models assist in understanding plant growth strategies and associated root phenes that have potential to be tested and introduced in physiological breeding programs. At the same time, we stress that it is necessary to carefully consider model assumptions and development from a whole plant-resource allocation perspective and to introduce or refine modules simulating explicitly root growth and architecture dynamics through ontogeny with reference to key factors that constrain root growth. In this view it is important to understand negative feedbacks such as plant-plant competition. We conclude by briefly touching on available and developing technologies for quantitative root phenotyping from lab to field, from quantification of partial root profiles in the field to 3D reconstruction of whole root systems. Finally, we discuss how these approaches can and should be tightly linked to modeling to explore the root phenome.

We present a novel method for deriving a structural model of a plant root system from 3D Magnetic Resonance Imaging (MRI) data of soil grown plants and use it for plant root system analysis. The structural model allows calculation of physiologically relevant parameters. Roughly speaking, MRI images show local water content of the investigated sample. The small, local amounts of water in roots require a relatively high resolution, which results in low SNR images. However, the spatial resolution of the MRI images remains coarse relative to the diameter of typical fine roots, causing many gaps in the visible root system. To reconstruct the root structure, we propose a three step approach: 1) detect tubular structures, 2) connect all pixels to the base of the root using Dijkstras algorithm, and 3) prune the tree using two signal strength related thresholds. Dijkstras algorithm determines the shortest path of each voxel to the base of the plant root, weighing the Euclidean distance measure by a multi-scale vesselness measure. As a result, paths running within good root candidates are preferred over paths in bare soil. We test this method using both virtually generated MRI images of Maize and real MRI images of Barley roots. In experiments on synthetic data, we show limitations of our algorithm with regard to resolution and noise levels. In addition we show how to use our reconstruction for root phenotyping on real MRI data of barley roots and snow pea in soil. Extending our conference publication [1], we show how to use the structural model to remove unwanted structures, like underground weeds.

International audience; Three-dimensional root architectural models emerged in the late 1980s, providing an opportunity to conceptualise and investigate that all important part of plants that is typically hidden and difficult to measure and study. These models have progressed from representing pre-defined root architectural arrangements, to simulating root growth in response to heterogeneous soil environments. This was done through incorporating soil properties and more complete descriptions of plant function, moving into the realm of functional-structural plant modelling. Modelling studies are often designed to investigate the relationship between root architectural traits and root distribution in soil, and the spatio-temporal variability of resource supply. Modelling root systems presents an opportunity to investigate functional tradeoffs between foraging strategies (i.e. shallow vs deep rooting) for contrasting resources (immobile versus mobile resources), and their dependence on soil type, rainfall and other environmental conditions. The complexity of the interactions between root traits and environment emphasises the need for models in which traits and environmental conditions can be independently manipulated, unlike in the real world. We provide an overview of the development of three-dimensional root architectural models from their origins, to their place today in the world of functional-structural plant modelling. The uses and capability of root architectural models to represent virtual plants and soil environment are addressed. We compare features of six current models, RootTyp, SimRoot, ROOTMAP, SPACSYS, R-SWMS, and RootBox, and discuss the future development of functional-structural root architectural modelling. Functional-structural root architectural models are being used to investigate numerous root-soil interactions, over a range of spatial scales. They are not only providing insights into the relationships between architecture, morphology and functional efficiency, but are also developing into tools that aid in the design of agricultural management schemes and in the selection of root traits for improving plant performance in specific environments.

SimRoot is a structural-functional model with, in contrast to most structural-functional models, emphasis on the architecture and function of the root system. We use SimRoot as a heuristic tool in our efforts to understand the function of different root traits for acquisition of nutrients and water in low fertility soils and drought environments. We parameterized SimRoot for both maize and bean and are currently able to simulate nutrient uptake for phosphorus and nitrogen in combination with water uptake and photosynthesis. SimRoot is designed to simulate stress responses in a virtual plant with a 'sound' carbon balance sheet. SimRoot is a modular model and can be run in different configurations. The main modules are root growth and shoot growth modules, carbon module, nutrient and water uptake and stress modules. The carbon module is based on LinTul. Both the Barber-Cushman model and Simunek's SWMS can be used for simulating nutrient uptake & transport in the soil. Nutrient stress effects growth by reducing for example the leaf area expansion rate or photosynthetic efficiency of the leaves. SimRoot is designed to work with measurable parameters and uses a predictor-corrected integration method to obtain accurate results, even when positive feedbacks are simulated. We have used SimRoot for