O<scp>pen</scp>S<scp>im</scp>R<scp>oot</scp>: widening the scope and application of root architectural models

Postma, J.; Kuppe, Christian; Owen, Markus R.; Mellor, Nathan; Griffiths, Marcus; Bennett, Malcolm J.; Lynch, Jonathan P.; Watt, Michelle

doi:10.1111/nph.14641

Cited by 173 publications

(110 citation statements)

References 65 publications

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“…Boundary conditions include a collar potential and, for each root segment, an outside water potential. We adapted the model to work with a growing root system and used a Newton solver to iteratively determine the collar potential such that the water uptake of the root system equals the potential transpiration, as estimated by the Penman-Monteith equation (Monteith, 1965; for details, see Postma et al, 2017). The outside water potential was obtained by coupling the root model to the soil model as described by Postma and Lynch (2011a).…”

Section: Model Descriptionmentioning

confidence: 99%

Root Cortical Senescence Improves Growth under Suboptimal Availability of N, P, and K

et al. 2017

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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.

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Section: Model Descriptionmentioning

confidence: 99%

Root Cortical Senescence Improves Growth under Suboptimal Availability of N, P, and K

et al. 2017

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“…One possible improvement to GRANAR would be to include a developmental module within the model, similar to the current root architectural models [52][53][54] .…”

Section: The Influence Of Anatomical Features Changes With the Develomentioning

confidence: 99%

GRANAR, a new computational tool to better understand the functional importance of root anatomy

Heymans

Couvreur

LaRue

et al. 2019

Preprint

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Root hydraulic conductivity is an important determinant of plant water uptake capacity. In particular, the root radial conductivity is often thought to be a limiting factor along the water pathways between the soil and the leaf. The root radial conductivity is itself defined by cell scale hydraulic properties and anatomical features. However, quantifying the influence of anatomical features on the radial conductivity remains challenging due to complex, and time-consuming, experimental procedures. We present a new computation tool, the Generator of Root ANAtomy in R (GRANAR) that can be used to rapidly generate digital versions of root anatomical networks. GRANAR uses a limited set of root anatomical parameters, easily acquired with existing image analysis tools. The generated anatomical network can then be used in combination with hydraulic models to estimate the corresponding hydraulic properties. Heymans et al. 2019 -GRANAR -A Generator of Root ANAtomy in R -Preprint We used GRANAR to re-analyse large maize (Zea mays) anatomical datasets from the literature. Our model was successful at creating virtual anatomies for each experimental observation. We also used GRANAR to generate anatomies not observed experimentally, over wider ranges of anatomical parameters. The generated anatomies were then used to estimate the corresponding radial conductivities with the hydraulic model MECHA. This enabled us to quantify the effect of individual anatomical features on the root radial conductivity. In particular, our simulations highlight the large importance of the width of the stele and the cortex. GRANAR is an open-source project available here: http://granar.github.io Keywords Root anatomy, radial conductivity, anatomical model, Zea mays , aerenchyma, reanalysis One-Sentence summary: Generator of Root ANAtomy in R (GRANAR) is a new open-source computational tool that can be used to rapidly generate digital versions of root anatomical networks.

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“…Intensive research has yielded detailed molecular and cellular mechanisms of how single roots grow at the local scale (reviewed in: (12,13)) on one hand, and the identification of global architectures, or ideotypes, that are best suited for resource capture in natural or agricultural environments (14)(15)(16)(17) on the other, but rarely have the two been experimentally connected. Structural-functional models can simulate a range of root architectures based on equations programmed to reproduce local growth and environmental interactions (18)(19)(20), and have been used to predict empirical data (21,22). However realistic parameterization and constraint of these models is thus far piecemeal, lacking adequate empirical data sets that incorporate time dynamics and genetically encoded differences in root development and genetic x environment interactions.…”

Section: Introductionmentioning

confidence: 99%

High-resolution 4D spatiotemporal analysis reveals the contributions of local growth dynamics to contrasting maize root architectures

Floro

Bray

et al. 2018

Preprint

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Root systems are branched networks that develop from simple growth properties of their individual roots. Yet a mature maize root system has many thousands of roots that each interact with soil structures, water and nutrient patches, and microbial ecologies in the microenvironments surrounding each root tip. Although the plasticity of root growth to these and other environmental factors is well known, how the many local processes contribute over time to global features of root system architecture is hardly understood. We employ an automated 3D root imaging pipeline to capture the growth of maize roots every four hours throughout seven days of seedling development. We model the contrasting architectures of two maize inbred genotypes and their hybrid to derive key parameters that distinguish complex growth patterns as a function of time. The statistical characteristics of local root growth defined the global system properties despite a large range of trait values. "Computational dissection" of a single root from each root system identified differences in the size of the root branching zone and lateral branching densities, but not radial patterns, that drove the contrasting root architectures from seedling to maturity. X-ray imaging of mature field-grown root crowns showed that seedling growth trajectories persisted throughout development and could predict eventual architectures, suggesting a strong genetic basis. The work connects individual and systemwide scales of root growth dynamics, providing the means for a function-valued approach to understanding the genetic and genetic x environment conditioning of root growth that will enable breeding for enhanced root traits. KEYWORDSroot architecture, growth modelling, multiscale, maize, genetics SIGNIFICANCE STATEMENTWhen and where roots grow determines their ability to capture short-lived and patchy water and nutrient resources to support the aboveground organs of the plant. Roots have no known longdistance external sensing mechanisms, but form branched networks that blindly explore the soil and respond to encountered local stimuli. How global architectures form from the many thousands of these local responses, and how they are controlled genetically are major open questions. Here we quantify differences in local root growth patterns of two inbred genotypes of maize that control contrasting systemwide properties. Measurements at the seedling stage were highly correlated with the complex architectures of mature root systems, paving the way for the development of crops with greater resource uptake capacity.

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OpenSimRoot: widening the scope and application of root architectural models

Cited by 173 publications

References 65 publications

Root Cortical Senescence Improves Growth under Suboptimal Availability of N, P, and K

Root Cortical Senescence Improves Growth under Suboptimal Availability of N, P, and K

GRANAR, a new computational tool to better understand the functional importance of root anatomy

High-resolution 4D spatiotemporal analysis reveals the contributions of local growth dynamics to contrasting maize root architectures

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