Abstract
AbstractTree structural and biomass growth studies mainly focus on the shoot compartment. Tree roots usually have to be taken apart due to the difficulties involved in measuring and observing this compartment, particularly root growth. In the context of climate change, the study of tree structural plasticity has become crucial and both shoot and root systems need to be considered simultaneously as they play a joint role in adapting traits to climate change (water availability for roots and light or carbon availability for shoots). We developed a botanically accurate whole-plant model and its simulator (RoCoCau) with a linkable external module (TOY) to represent shoot and root compartment dependencies and hence tree structural plasticity in different air and soil environments. This paper describes a new deep neural network calibration trained on simulated data sets computed from a set of more than 360 000 random TOY parameter values and random climate values. These data sets were used for training and for validation. For this purpose, we chose VoxNet, a convolutional neural network designed to classify 3D objects represented as a voxelized scene. We recommend further improvements for VoxNet inputs, outputs and training. We were able to teach the network to predict the value of environment data well (mean error < 2 %), and to predict the value of TOY parameters for plants under water stress conditions (mean error < 5 % for all parameters), and for any environmental growing conditions (mean error < 20 %).
Highlights
Plant growth modelling can be useful to predict plant biomass production and to study biological processes that take place throughout the lifetime of the plant
Here we consider the ability of the tree to adapt to resource availability by modulating biomass partitioning as defined in optimal partitioning theory (Niklas and Enquist 2002; Reich 2002; Colter Burkes et al 2003): resources are allocated to both compartments in order to balance their contribution capabilities
To disentangle the two effects, we developed TOY, an extra model that represents (i) biomass production according to current plant structure and environmental constraints, (ii) biomass partitioning and its influence on plant growth and architectural development
Summary
Plant growth modelling can be useful to predict plant biomass production and to study biological processes that take place throughout the lifetime of the plant. 2 Masson et al. The question we aimed to answer with the present study is: what is plant plasticity with respect to biomass production and partitioning considering both shoot and root systems?. Trees have the capacity to favour the growth of certain axes at the expense of others (e.g. trunk, stump), by partitioning biomass between the root and shoot compartments. Under water stress, the tree allocates a larger share of the biomass produced to the root system, allowing more absorption of water. Under light stress, the tree allocates a larger share of the biomass to the shoot system, allowing better absorption of light. Here we consider the ability of the tree to adapt to resource availability by modulating biomass partitioning as defined in optimal partitioning theory (Niklas and Enquist 2002; Reich 2002; Colter Burkes et al 2003): resources are allocated to both compartments in order to balance their contribution capabilities
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