Abstract
State-of-the-Art models of Root System Architecture (RSA) do not allow simulating root growth around rigid obstacles. Yet, the presence of obstacles can be highly disruptive to the root system. We grew wheat seedlings in sealed petri dishes without obstacle and in custom 3D-printed rhizoboxes containing obstacles. Time-lapse photography was used to reconstruct the wheat root morphology network. We used the reconstructed wheat root network without obstacle to calibrate an RSA model implemented in the R-SWMS software. The root network with obstacles allowed calibrating the parameters of a new function that models the influence of rigid obstacles on wheat root growth. Experimental results show that the presence of a rigid obstacle does not affect the growth rate of the wheat root axes, but that it does influence the root trajectory after the main axis has passed the obstacle. The growth recovery time, i.e. the time for the main root axis to recover its geotropism-driven growth, is proportional to the time during which the main axis grows along the obstacle. Qualitative and quantitative comparisons between experimental and numerical results show that the proposed model successfully simulates wheat RSA growth around obstacles. Our results suggest that wheat roots follow patterns that could inspire the design of adaptive engineering flow networks.
Highlights
State-of-the-Art models of Root System Architecture (RSA) do not allow simulating root growth around rigid obstacles
Numerical models of root growth in a soil matrix can be grouped into two categories: (1) continuum models, in which field variables, such as root length density and root apical meristems are defined by means of probability density functions that are updated through diffusion differential equations, to reflect root growth[7,8,9,10]; (2) discrete models, in which the components of the RSA are explicitly described, and in which the growth of RSA depends both on the soil environment and on predefined input variables that are specific to the genetics of the species under study[11,12,13,14,15,16,17,18]
RootTyp[16] allows simulating specific growth patterns for different plant species; SimRoot[17] requires fewer assumptions on the growth pattern and assigns a time-dependent probability density function to each input parameter; R ootMap[11,12] includes a variety of functions to account for the diverse interactions between the root and the soil environment; SPACSYS18 focuses on the distribution of photosynthate from the shoot; R-SWMS14,15 is the state-of-art code that accounts for solute and water flow in soil and root system; the RootBox[13] employs a novel L-system to mathematically describe the RSA under the assumption that the morphology of the RSA is self-similar; and the DigR19 features RSA analysis and root type classification
Summary
State-of-the-Art models of Root System Architecture (RSA) do not allow simulating root growth around rigid obstacles. In addition to the root growth rate, root branching angle, and root branch spacing, which can be determined experimentally, the R-SWMS model requires four more input parameters: the tip delay time (defined as the time needed for the root to grow its tip from the closest branching point, as shown in Fig. 1a), the relative weight of geotropism on the root growth direction, the relative weight of the substrate penetration resistance gradient on the root growth direction and the maximum random deviation angle (used to mimic the dynamic exploration of the root tip).
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