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Schnepf et al., (2020) defined benchmark scenarios for root growth models, soil water flow models, root water flow models, and for water flow in the coupled soil-root system. All benchmarks and corresponding reference solutions were published in the form of Jupyter Notebooks on the GitHub repository https://github.com/RSAbenchmarks/collaborative-comparison. Several groups of functional-structural model developers have joined this benchmarking activity and provided the results of their individual implementations of the different scenarios. The focus of this contribution is on water uptake from a drying soil by a static root architecture. The numerical solutions of the different participating simulators as compared to the provided reference solution. The participating simulators are CPlantBox, DuMux, R-SWMS, OpenSimRoot and SRI. They have in common that they simulate water flow in the 3D soil domain, water flow inside the root system that is represented as a mathematical tree graph, and the coupling between the two domains in form of a volumetric sink term that describes the transfer of water between the two domains. The simulators differ in the numerical schemes used for solving the water flow equations in roots and soil domains, as well as in the way the sink term is formulated, in particular in the way the possibly increased rhizosphere resistance to water flow is accounted for. The results to the water flow in soil benchmarks show how the different simulators perform against the analytical solution to a problem of infiltration into an initially dry soil, as well as a problem of evaporation from initially moist soil. All of the simulators could accurately predict the infiltration front in different soil types as well as the actual evaporation curves. The coupled problem of root water uptake by a static root architecture from an initially already dry soil posed a bigger challenge to the different simulators and revealed some diversity between the different solutions. The Benchmark with an initially rather dry soil defined a potential transpiration that immediately induced water stress of the plant. The simulators had to simulate the consequent rhizosphere drying and associated increase in rhizosphere resistance. All of the soil simulators smoothed the gradients in the rhizosphere at the soil grid size such that root water uptake was significantly overestimated unless the rhizosphere resistance was explicitly accounted for in the root water uptake model. As a result, all simulators came close to the reference solution (that itself is a numerical solution, see Schnepf et al. 2020 for details). In this study, we showed that all simulators are generally able to solve the benchmark problems but minor differences occur amongst the simulators when simulating different soil types. Benchmarking led to model improvements and helped interpret model results in a more informed way. The availability of “reference solutions“ made modellers aware of the range of validity of their numerical solution and encouraged them to improve either their numerical solution or to introduce new processes Future efforts may aim to extend the benchmarks from water flow to further processes, such as solute transport or rhizodeposition. Schnepf et al., 2020, Front. Plant Sci.

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 and Aims Although root penetration of strong soils has been intensively studied at the scale of individual root axes, interactions between soil physical properties and soil foraging by whole plants are less clear. Here we investigate how variation in the penetration ability of distinct root classes and bulk density profiles common to real-world soils interact to affect soil foraging strategies. Methods We utilize the functional–structural plant model ‘OpenSimRoot’ to simulate the growth of maize (Zea mays) root systems with variable penetration ability of axial and lateral roots in soils with (1) uniform bulk density, (2) plow pans and (3) increasing bulk density with depth. We also modify the availability and leaching of nitrate to uncover reciprocal interactions between these factors and the capture of mobile resources. Key Results Soils with plow pans and bulk density gradients affected overall size, distribution and carbon costs of the root system. Soils with high bulk density at depth impeded rooting depth and reduced leaching of nitrate, thereby improving the coincidence of nitrogen and root length. While increasing penetration ability of either axial or lateral root classes produced root systems of comparable net length, improved penetration of axial roots increased allocation of root length in deeper soil, thereby amplifying N acquisition and shoot biomass. Although enhanced penetration ability of both root classes was associated with greater root system carbon costs, the benefit to plant fitness from improved soil exploration and resource capture offset these. Conclusions While lateral roots comprise the bulk of root length, axial roots function as a scaffold determining the distribution of these laterals. In soils with high soil strength and leaching, root systems with enhanced penetration ability of axial roots have greater distribution of root length at depth, thereby improving capture of mobile resources.

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.

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 architecture drives plant ecology and physiology, but current detection methods limit understanding of root placement within soil profiles. We developed a statistical model of root volume along depth gradients and used it to infer carbon storage potential of land-use changes from conventional agriculture to perennial bioenergy grasses. We estimated root volume of maize-soybean rotation and three perennial grass systems (Miscanthus × giganteus, Panicum virgatum, tallgrass prairie mix) by Bayesian modeling from minirhizotron images, correcting for small images and near-surface underdetection. We monitored seasonal and inter-annual changes in root volume distribution, then validated our estimates against root mass from core samples. The model explained 29% of root volume variation and validated well against core mass. Seventh-year perennials had greater belowground biomass than maize-soybean both in total (11-16×) and throughout the profile (2-17× at every depth

Warming temperatures and increasing CO2 are likely to have large effects on the amount of carbon stored in soil, but predictions of these effects are poorly constrained. We elevated temperature (canopy: +2.8 °C; soil growing season: +1.8 °C; soil fallow: +2.3 °C) for 3 years within the 9th-11th years of an elevated CO2 (+200 ppm) experiment on a maize-soybean agroecosystem, measured respiration by roots and soil microbes, and then used a process-based ecosystem model (DayCent) to simulate the decadal effects of warming and CO2 enrichment on soil C. Both heating and elevated CO2 increased respiration from soil microbes by ~20%, but heating reduced respiration from roots and rhizosphere by ~25%. The effects were additive, with no heat × CO2 interactions. Particulate organic matter and total soil C declined over time in all treatments and were lower in elevated CO2 plots than in ambient plots, but did not differ between heat treatments. We speculate that these declines indicate a priming effect, with increased C inputs under elevated CO2 fueling a loss of old soil carbon. Model simulations of heated plots agreed with our observations and predicted loss of ~15% of soil organic C after 100 years of heating, but simulations of elevated CO2 failed to predict the observed C losses and instead predicted a ~4% gain in soil organic C under any heating conditions. Despite model uncertainty, our empirical results suggest that combined, elevated CO2 and temperature will lead to long-term declines in the amount of carbon stored in agricultural soils.

A B S T R A C T Replacing annual row crops with perennial grasses for bioenergy represents a landscape-level change in species composition, with the potential to impact annual soil nutrient removal on a regional scale. In this study we measured the concentration of ten essential nutrients in harvested material from three potential perennial bioenergy crops: Panicum virgatum L., Miscanthusgiganteus, and a reestablished prairie to determine annual soil nutrient removals. We compared perennial bioenergy crops to nutrient removals by annual cropping systems of Zea mays L. (maize) and Glycine max L. (soybean) in Illinois. Crops were grown under management practices typical for the Midwest, US. In addition, we examined geographic variation in nutrient removal of M. � giganteus at four US locations. Total removal of N, P, K, Ca, Mg, S, Fe, Mn, Na, and Zn was significantly greater in maize than in any of the perennials. Removal of N, P, and K in M. � giganteus was 3.7, 1.8, and 1.8% of the removal in maize, and 49.0, 17.4, and 31.9% of the removal in soybean respectively. At sites in Illinois, Kentucky, Nebraska, and New Jersey we found differences in N and K removal by M. � giganteus that corresponded with differences in biomass. There was no effect of fertilization on M. � giganteus biomass, but removal of N, S, and Mg increased and P removal decreased with increasing rates of urea fertilization. Cultivation of M. � giganteus and switchgrass on land formerly used for row crops may reduce the need for nutrient additions and potential losses of nutrients to groundwater and the atmosphere.

Belowground carbon (C) dynamics of terrestrial ecosystems play an important role in the global C cycle and thereby in climate regulation. Globally, land-use change is a major driver of changes in belowground C storage. The emerging bioenergy industry is likely to drive widespread land-use changes, including the replacement of annually tilled croplands with perennial bioenergy crops, and thereby to impact the climate system through alteration of belowground C dynamics. Mechanistic understanding of how land-use changes impact belowground C storage requires elucidation of changes in belowground C flows; however, altered belowground C dynamics following land-use change have yet to be thoroughly quantified through field measurements. Here, we show that belowground C cycling pathways of establishing perennial bioenergy crops (0- to 3.5-year-old miscanthus, switchgrass, and a native prairie mix) were substantially altered relative to row crop agriculture (corn-soy rotation); specifically, there were substantial increases in belowground C allocation (>400%), belowground biomass (400–750%), root-associated respiration (up to 2,500%), moderate reductions in litter inputs (20–40%), and respiration in root-free soil (up to 50%). This more active root-associated C cycling of perennial vegetation provides a mechanism for observed net C sequestration by these perennial ecosystems, as well as commonly observed increases in soil C under perennial bioenergy crops throughout the world. The more active root-associated belowground C cycle of perennial vegetation implies a climate benefit of grassland maintenance or restoration, even if biomass is harvested annually for bioenergy production.

Warming temperatures and increasing CO

Phytochrome A is the primary photoreceptor for mediating various far-red light-induced responses in higher plants. We recently showed that Arabidopsis (Arabidopsis thaliana) FAR-RED ELONGATED HYPOCOTYL3 (FHY3) and FAR-RED-IMPAIRED RESPONSE1 (FAR1), a pair of homologous proteins sharing significant sequence homology to Mutator-like transposases, act as novel transcription factors essential for activating the expression of FHY1 and FHL (for FHY1-like), whose products are required for light-induced phytochrome A nuclear accumulation and subsequent light responses. FHY3, FAR1, and Mutator-like transposases also share a similar domain structure, including an N-terminal C2H2 zinc finger domain, a central putative core transposase domain, and a C-terminal SWIM motif (named after SWI2/SNF and MuDR transposases). In this study, we performed a promoter-swapping analysis of FHY3 and FAR1. Our results suggest that the partially overlapping functions of FHY3 and FAR1 entail divergence of their promoter activities and protein subfunctionalization. To gain a better understanding of the molecular mode of FHY3 function, we performed a structure-function analysis, using site-directed mutagenesis and transgenic approaches. We show that the conserved N-terminal C2H2 zinc finger domain is essential for direct DNA binding and biological function of FHY3 in mediating light signaling, whereas the central core transposase domain and C-terminal SWIM domain are essential for the transcriptional regulatory activity of FHY3 and its homodimerization or heterodimerization with FAR1. Furthermore, the ability to form homodimers or heterodimers largely correlates with the transcriptional regulatory activity of FHY3 in plant cells. Together, our results reveal discrete roles of the multiple domains of FHY3 and provide functional support for the proposition that FHY3 and FAR1 represent transcription factors derived from a Mutator-like transposase(s).