CRootBox: a structural–functional modelling framework for root systems

Schnepf, Andrea; Leitner, Daniel; Landl, Magdalena; Lobet, Guillaume; Hieu, Trung; Morandage, Shehan; Cheng, Shuiyuan; Zörner, Mirjam; Vanderborght, Jan; Vereecken, Harry

doi:10.1093/aob/mcx221

Cited by 136 publications

(135 citation statements)

References 54 publications

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“…For future work, it will be interesting to implement our model within a spatially explicit, branching root architecture such as typically used in FSP models (for example (Schnepf et al, 2018)). Additionally, an interesting direction would be to attempt to extend the explanatory power of the model to the apparent sensitivity of root growth to temporal changes in nitrate levels (Shemesh et al, 2010a(Shemesh et al, , 2010b(Shemesh et al, , 2011.…”

Section: Discussionmentioning

confidence: 99%

Modeling of root nitrate responses suggests preferential foraging arises from the integration of demand, supply and local presence signals

Boer

Teixeira

Tusscher

2019

Preprint

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A plants' fitness to a large extent depends on its capacity to adapt to spatio-temporally varying environmental conditions. One such environmental condition to which plants display extensive phenotypic plasticity is soil nitrate levels and patterns. In response to heterogeneous nitrate distribution, plants show a preferential foraging response, enhancing root growth in high nitrate patches and repressing root growth in low nitrate locations beyond a level that can be explained from local nitrate sensing. Although various molecular players involved in this preferential foraging behavior have been identified, how these together shape root system adaptation has remained unresolved. Here we use a simple modeling approach in which we incrementally incorporate the various known molecular pathways to investigate the combination of regulatory mechanisms that underly preferential root nitrate foraging. Our model suggests that instead of a thus far not discovered growth suppressing supply signal, growth reduction on the low nitrate side may simply arise from a reduced root foraging and increased competition for carbon. Additionally, our work suggests that the long distance CK signaling involved in root growth increase in high nitrate patches may represent a supply signal specifically functioning in modulating demand signaling strength. We illustrate how this integration of demand and supply signals prevents excessive preferential foraging under conditions in which demand is not met by sufficient supply and a more generic foraging in search of nitrate should be maintained.

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Section: Discussionmentioning

confidence: 99%

Modeling of root nitrate responses suggests preferential foraging arises from the integration of demand, supply and local presence signals

Boer

Teixeira

Tusscher

2019

Preprint

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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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“…To 2 tackle this challenge, a dynamic structural-functional root model (i.e. CRootBox) was previously 3 developed by Schnepf et al (2018) enabling the integration of multiple root phenes. A second step would 4 then be to link such integrated virtual root system with a soil-water model, in order to acquire insights in 5 root performance and unravel the underlying processes of water and P uptake.…”

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confidence: 99%

Water and phosphorus uptake by upland rice root systems unraveled under multiple scenarios: linking a 3D soil-root model and data

Bauw

Mai

Schnepf

et al. 2020

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2Background and aims 3 Upland rice is often grown where water and phosphorus (P) are limited and these two factors 4 interact on P bioavailability. To better understand this interaction, mechanistic models 5 representing small-scale nutrient gradients and water dynamics in the rhizosphere of full-grown 6 root systems are needed. 7 Methods 8Rice was grown in large columns using a P-deficient soil at three different P supplies in the 9 topsoil (deficient, suboptimal, non-limiting) in combination with two water regimes (field 10 capacity versus drying periods). Root architectural parameters and P uptake were determined. 11Using a multiscale model of water and nutrient uptake, in-silico experiments were conducted by 12 mimicking similar P and water treatments. First, 3D root systems were reconstructed by 13 calibrating an architecure model with observed phenological root data, such as nodal root 14 number, lateral types, interbranch distance, root diameters, and root biomass allocation along 15 depth. Secondly, the multiscale model was informed with these 3D root architectures and the 16 actual transpiration rates. Finally, water and P uptake were simulated. 17 Key results 18The plant P uptake increased over threefold by increasing P and water supply, and drying periods 19 reduced P uptake at high but not at low P supply. Root architecture was significantly affected by 20 the treatments. Without calibration, simulation results adequately predicted P uptake, including 21 the different effects of drying periods on P uptake at different P levels. However, P uptake was 22 underestimated under P deficiency, a process likely related to an underestimated affinity of P 23 uptake transporters in the roots. Both types of laterals (i.e. S-and L-type) are shown to be highly 24 important for both water and P uptake, and the relative contribution of each type depend on both 25 soil P availability and water dynamics. Key drivers in P uptake are growing root tips and the 26 distribution of laterals. 27 Conclusions 28 This model-data integration demonstrates how multiple co-occurring single root phene responses 29 to environmental stressors contribute to the development of a more efficient root system. Further 30 model improvements such as the use of Michaelis constants from buffered systems and the 31 inclusion of mycorrhizal infections and exudates are proposed. 32 33 34

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CRootBox: a structural–functional modelling framework for root systems

Cited by 136 publications

References 54 publications

Modeling of root nitrate responses suggests preferential foraging arises from the integration of demand, supply and local presence signals

Modeling of root nitrate responses suggests preferential foraging arises from the integration of demand, supply and local presence signals

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

Water and phosphorus uptake by upland rice root systems unraveled under multiple scenarios: linking a 3D soil-root model and data

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