The first architectural root model was published in 1973 by Lungley. By 1988, ROOTMAP was being used for 3D modeling of crop root systems. While RSA modeling became more complex, more powerful computers and visualization software were also developed. Better models and visualization software together give rise to the evolution of root system architecture models in the following figure.
|Figures are from their respective creators, cited below each image. Figures compiled by RBN.|
Limitations of current models also imply the future directions. Though RSA modeling is being used to understand plant uptake of soil resources, there are problems with up-scaling from single roots to the whole system, especially in defining a useful voxel (3D unit of simulated soil) size. Current models do not include rhizosphere processes or soil microorganisms that are known to be important for resource uptake and root growth. However, as these limitations are addressed, combinations of root and shoot models will lead to true virtual plants.
These six root models were compared head-to-head in this review for a variety of their uses and methods. The paper's authors summarized the models' major differences by their focuses:
RootTyp - detailed, dynamic, and can be used to study many species, including trees
SimRoot - resource acquisition as influenced by specific traits and resource utilization
ROOTMAP - root system plasticity and root proliferation in local patches
SPACSYS - crop modeling with predictions of biomass and yield
R-SWMS - root and soil hydrology to study how water uptake is influenced by RSA
RootBox - L-systems model in Matlab and publicly available
Details of their uses and differences can be found in the paper. The authors conclude by stressing the importance of the models taking into account soil microorganisms and internal plant processes such as hormonal signaling. These models are ready to be used more broadly and in collaboration with plant biologists specializing in other fields. Root system architecture and soil modeling is positioned to offer substantial value to basic and applied science through ecosystem design and crop breeding.