Modelling root–soil interactions using three–dimensional models of root growth, architecture and function

Dunbabin, Vanessa M.; Postma, J.; Schnepf, Andrea; Pagès, Loïc; Javaux, Mathieu; Wu, Lianhai; Leitner, Daniel; Chen, Yinglong; Rengel, Zed; Diggle, Art

doi:10.1007/s11104-013-1769-y

Cited by 262 publications

(224 citation statements)

References 168 publications

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“…This simplification could partly explain the model overestimation of SOC stocks in the 0.0-0.1 m layer of the alleys compared to observed data. This result suggests that it could be useful to couple the CAR-BOSAF model with a model describing root architecture and root growth (Dunbabin et al, 2013;Dupuy et al, 2010), for instance using voxel automata (Mulia et al, 2010). Moreover, the model described a slight increase in SOC stocks in the middle of the alleys rather than close to the trees in the alleys.…”

Section: Representation Of Soc Spatial Heterogeneity In Agroforestrymentioning

confidence: 96%

High organic inputs explain shallow and deep SOC storage in a long-term agroforestry system – combining experimental and modeling approaches

et al. 2018

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Abstract. Agroforestry is an increasingly popular farming system enabling agricultural diversification and providing several ecosystem services. In agroforestry systems, soil organic carbon (SOC) stocks are generally increased, but it is difficult to disentangle the different factors responsible for this storage. Organic carbon (OC) inputs to the soil may be larger, but SOC decomposition rates may be modified owing to microclimate, physical protection, or priming effect from roots, especially at depth. We used an 18-year-old silvoarable system associating hybrid walnut trees (Juglans regia × nigra) and durum wheat (Triticum turgidum L. subsp. durum) and an adjacent agricultural control plot to quantify all OC inputs to the soil -leaf litter, tree fine root senescence, crop residues, and tree row herbaceous vegetation -and measured SOC stocks down to 2 m of depth at varying distances from the trees. We then proposed a model that simulates SOC dynamics in agroforestry accounting for both the whole soil profile and the lateral spatial heterogeneity. The model was calibrated to the control plot only.Measured OC inputs to soil were increased by about 40 % (+ 1.11 t C ha −1 yr −1 ) down to 2 m of depth in the agroforestry plot compared to the control, resulting in an additional SOC stock of 6.3 t C ha −1 down to 1 m of depth. However, most of the SOC storage occurred in the first 30 cm of soil and in the tree rows. The model was strongly validated, properly describing the measured SOC stocks and distribution with depth in agroforestry tree rows and alleys. It showed that the increased inputs of fresh biomass to soil explained the observed additional SOC storage in the agroforestry plot. Moreover, only a priming effect variant of the model was able to capture the depth distribution of SOC stocks, suggesting the priming effect as a possible mechanism driving deep SOC dynamics. This result questions the potential of soils to store large amounts of carbon, especially at depth. Deep-rooted trees modify OC inputs to soil, a process that deserves further study given its potential effects on SOC dynamics.

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Section: Representation Of Soc Spatial Heterogeneity In Agroforestrymentioning

confidence: 96%

High organic inputs explain shallow and deep SOC storage in a long-term agroforestry system – combining experimental and modeling approaches

et al. 2018

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“…In this way, models are created that include state-of-the-art knowledge and have significant parameters. There are various root architectural models incorporating a multitude of processes (Dunbabin et al, 2013) that are originally based on Pagès et al (1989) and Diggle (1988). Generally, the parameterization of such models is difficult and demands elaborate experimental effort.…”

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confidence: 99%

Recovering Root System Traits Using Image Analysis Exemplified by Two-Dimensional Neutron Radiography Images of Lupine

et al. 2013

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Root system traits are important in view of current challenges such as sustainable crop production with reduced fertilizer input or in resource-limited environments. We present a novel approach for recovering root architectural parameters based on imageanalysis techniques. It is based on a graph representation of the segmented and skeletonized image of the root system, where individual roots are tracked in a fully automated way. Using a dynamic root architecture model for deciding whether a specific path in the graph is likely to represent a root helps to distinguish root overlaps from branches and favors the analysis of root development over a sequence of images. After the root tracking step, global traits such as topological characteristics as well as root architectural parameters are computed. Analysis of neutron radiographic root system images of lupine (Lupinus albus) grown in mesocosms filled with sandy soil results in a set of root architectural parameters. They are used to simulate the dynamic development of the root system and to compute the corresponding root length densities in the mesocosm. The graph representation of the root system provides global information about connectivity inside the graph. The underlying root growth model helps to determine which path inside the graph is most likely for a given root. This facilitates the systematic investigation of root architectural traits, in particular with respect to the parameterization of dynamic root architecture models.

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“…Their review discusses the potential of various techniques, including the use of transparent soil, to provide better understanding of root-soil interactions. Other reviews in this field deal with modelling of rhizosphere and plant-soil interactions (Hinsinger et al 2011;Dunbabin et al 2013), mycorrhizae (Treseder 2013), mycorrhizal nitrogen uptake (Hodge and Storer 2015), and transport processes in porous media (Wildenschild and Sheppard 2013). Finally, there is a collection of articles published in edited books (Anderson and Hopmans 2013;Bengough 2012;Timlin and Ahuja 2013) covering issues related to this review, such as neutron and X-ray imaging.…”

Section: Existing Work On Rhizosphere Imaging and Modelingmentioning

confidence: 99%

“…The new state of the art approach to modelling rootsoil interaction is based on root system architecture, i.e., models which take into account the specifics of root system architecture at the expense of high computational cost (Dunbabin et al 2013;Ge et al 2000;Pages 2011). While root system architecture has in the past been derived from a range of computational models, it is now possible to measure it in situ (i.e., in the soil) using imaging techniques such as magnetic resonance imaging (MRI), X-ray computed tomography (X-ray CT) and neutron tomography, see Carminati et al (2010); Gregory et al (2003); Koebernick et al (2014);Metzner et al (2015); Mooney et al (2012); Moradi et al (2011);Oswald et al (2008) as a good starting point for the literature.…”

Section: Modelling Rhizosphere Processes: State Of the Artmentioning

confidence: 99%

Challenges in imaging and predictive modeling of rhizosphere processes

et al. 2016

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Background Plant-soil interaction is central to human food production and ecosystem function. Thus, it is essential to not only understand, but also to develop predictive mathematical models which can be used to assess how climate and soil management practices will affect these interactions. Scope In this paper we review the current developments in structural and chemical imaging of rhizosphere processes within the context of multiscale mathematical image based modeling. We outline areas that need more research and areas which would benefit from more detailed understanding. Conclusions We conclude that the combination of structural and chemical imaging with modeling is an incredibly powerful tool which is fundamental for understanding how plant roots interact with soil. We emphasize the need for more researchers to be attracted to this area that is so fertile for future discoveries. Finally, model building must go hand in hand with experiments. In particular, there is a real need to integrate rhizosphere structural and chemical imaging with modeling for better understanding of the rhizosphere processes leading to models which explicitly account for pore scale processes.

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Modelling root–soil interactions using three–dimensional models of root growth, architecture and function

Cited by 262 publications

References 168 publications

High organic inputs explain shallow and deep SOC storage in a long-term agroforestry system – combining experimental and modeling approaches

High organic inputs explain shallow and deep SOC storage in a long-term agroforestry system – combining experimental and modeling approaches

Recovering Root System Traits Using Image Analysis Exemplified by Two-Dimensional Neutron Radiography Images of Lupine

Challenges in imaging and predictive modeling of rhizosphere processes

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