Biology subjects

Vereecken, H.

Publications and source records attributed to Vereecken, H..

7 recordsLinked to original sources

bioRxiv2023

Development and calibration of the FSPM CPlantBox to represent the interactions between water and carbon fluxes in the soil-plant-atmosphere continuum

A plants development is strongly linked to the water and carbon flows in the soil-plant-atmosphere continuum. Expected climate shifts will alter the water and carbon cycles and will affect plant phenotypes. Comprehensive models which simulate mechanistically and dynamically the feedback loops between a plants three-dimensional development and the water and carbon flows are useful tools to evaluate the sustainability of genotype-environment-management combinations which do not yet exist. In this study, we present the latest version of the open-source three-dimensional Functional-Structural Plant Model CPlantBox with PiafMunch and DuMux coupling. We simulated semi-mechanistically the development of generic C3 monocots from 10 to 25 days after sowing and undergoing an atmospheric dry spell of one week (no precipitation). We compared the results for dry spells starting on different days (day 11 or 18) and with different climates (wetter and colder against drier and warmer atmospheric and initial soil conditions). Compared with the wetter and colder climate, the dry spell with the drier and warmer climate led to a lower instantaneous water use efficiency. Moreover, the lower symplasm turgor for the drier and warmer climate limited the growth, which made the sucrose available for other processes, such as maintenance respiration. Both of these effects were stronger for the later dry spell compared with the early dry spell under the drier and warmer climate. We could thus use CPlantBox to simulate diverging emerging processes (like carbon partitioning) defining the plants phenotypic plasticity response to their environment.

plant biology↗

bioRxiv2022

Phloem anatomy restricts root system architecture development: theoretical clues from in silico experiments.

Plant growth and development involve the integration of numerous processes, influenced by both endogenous and exogenous factors. At any given time during a plants life cycle, the plant architecture is a readout of this continuous integration. However, untangling the individual factors and processes involved in the plant development and quantifying their influence on the plant developmental process is experimentally challenging. Here we used a combination of computational plant models to help understand experimental findings about how local phloem anatomical features influence the root system architecture. In particular, we simulated the mutual interplay between the root system architecture development and the carbohydrate distribution to provide a plausible mechanistic explanation for several experimental results. Our in silico study highlighted the strong influence of local phloem hydraulics on the root growth rates, growth duration and final length. The model result showed that a higher phloem resistivity leads to shorter roots due to the reduced flow of carbon within the root system. This effect was due to local properties of individual roots, and not linked to any of the pleiotropic effects at the root system level. Our results open the door to a better representation of growth processes in plant computational models.

plant biology↗

bioRxiv2021

Combining deep learning and automated feature extraction to analyze minirhizotron images: development and validation of a new pipeline

Root systems of crops play a significant role in agro-ecosystems. The root system is essential for water and nutrient uptake, plant stability, symbiosis with microbes and a good soil structure. Minirhizotrons, consisting of transparent tubes that create windows into the soil, have shown to be effective to non-invasively investigate the root system. Root traits, like root length observed around the tubes of minirhizotron, can therefore be obtained throughout the crop growing season. Analyzing datasets from minirhizotrons using common manual annotation methods, with conventional software tools, are time consuming and labor intensive. Therefore, an objective method for high throughput image analysis that provides data for field root-phenotyping is necessary. In this study we developed a pipeline combining state-of-the-art software tools, using deep neural networks and automated feature extraction. This pipeline consists of two major components and was applied to large root image datasets from minirhizotrons. First, a segmentation by a neural network model, trained with a small image sample is performed. Training and segmentation are done using "Root-Painter". Then, an automated feature extraction from the segments is carried out by "RhizoVision Explorer". To validate the results of our automated analysis pipeline, a comparison of root length between manually annotated and automatically processed data was realized with more than 58,000 images. Mainly the results show a high correlation (R=0.81) between manually and automatically determined root lengths. With respect to the processing time, our new pipeline outperforms manual annotation by 98.1 - 99.6 %. Our pipeline,combining state-of-the-art software tools, significantly reduces the processing time for minirhizotron images. Thus, image analysis is no longer the bottle-neck in high-throughput phenotyping approaches.

plant biology↗

bioRxiv2021

Simulating rhizodeposition patterns around growing and exuding root systems

1In this study, we developed a novel model approach to compute the spatio-temporal distribution patterns of rhizodeposits around growing root systems in three dimensions. This model approach allows us for the first time to study the evolution of rhizodeposition patterns around complex three-dimensional root systems. Root systems were generated using the root architecture model CPlantBox. The concentration of rhizodeposits at a given location in the soil domain was computed analytically. To simulate the spread of rhizodeposits in the soil, we considered rhizodeposit release from the roots, rhizodeposit diffusion into the soil, rhizodeposit sorption to soil particles, and rhizodeposit degradation by microorganisms. To demonstrate the capabilities of our new model approach, we performed simulations for the two example rhizodeposits mucilage and cit-rate and the example root system Vicia faba. The rhizodeposition model was parameterized using values from the literature. Our simulations showed that the rhizosphere soil volume with rhizodeposit concentrations above a defined threshold value (i.e., the rhizodeposit hotspot volume), exhibited a maximum at intermediate root growth rates. Root branching allowed the rhizospheres of individual roots to overlap, resulting in a greater volume of rhizodeposit hotspots. This was particularly important in the case of citrate, where overlap of rhizodeposition zones accounted for more than half of the total rhizodeposit hotspot volumes. Coupling a root architecture model with a rhizodeposition model allowed us to get a better understanding of the influence of root architecture as well as rhizodeposit properties on the evolution of the spatio-temporal distribution patterns of rhizodeposits around growing root systems.

plant biology↗

bioRxiv2019

CPlantBox, a whole plant modeling framework for the simulation of water and carbon related processes

The interaction between carbon and flows within the plant is at the center of most growth and developmental processes. Understanding how these fluxes influence each other, and how they respond to heterogeneous environmental conditions, is important to answer diverse questions in forest, agriculture and environmental sciences. However, due to the high complexity of the plant-environment system, specific tools are needed to perform such quantitative analyses.\n\nHere we present CPlantBox, full plant modelling framework based on the root system model CRootBox. CPlantbox is capable of simulating the growth and development of a variety of plant architectures (root and shoot). In addition, the flexibility of CPlantBox enables its coupling with external modeling tools. Here, we connected it to an existing mechanistic model of water and carbon flows in the plant, PiafMunch.\n\nThe usefulness of the CPlantBox modelling framework is exemplified in four case studies. Firstly, we illustrate the range of plant structures that can be simulated using CPlantBox. In the second example, we simulated diurnal carbon and water flows, which corroborates published experimental data. In the third case study, we simulated impacts of heterogeneous environment on carbon and water flows. Finally, we showed that our modelling framework can be used to fit phloem pressure and flow speed to (published) experimental data.\n\nThe CPlantBox modelling framework is open-source, highly accessible and flexible. Its aim is to provide a quantitative framework for the understanding of plant-environment interaction.

plant biology↗

bioRxiv2019

Call for participation: Collaborative benchmarking of functional-structural root architecture models. The case of root water uptake.

Three-dimensional models of root growth, architecture and function are becoming important tools that aid the design of agricultural management schemes and the selection of beneficial root traits. However, while benchmarking is common in many disciplines that use numerical models such as natural and engineering sciences, functional-structural root architecture models have never been systematically compared. The following reasons might induce disagreement between the simulation results of different models: different representation of root growth, sink term of root water and solute uptake and representation of the rhizosphere. Presently, the extent of discrepancies is unknown, and a framework for quantitatively comparing functional-structural root architecture models is required. We propose, in a first step, to define benchmarking scenarios that test individual components of complex models: root architecture, water flow in soil and water flow in roots. While the latter two will focus mainly on comparing numerical aspects, the root architectural models have to be compared at a conceptual level as they generally differ in process representation. Therefore defining common inputs that allow recreating reference root systems in all models will be a key challenge. In a second step, benchmarking scenarios for the coupled problems are defined. We expect that the results of step 1 will enable us to better interpret differences found in step 2. This benchmarking will result in a better understanding of the different models and contribute towards improving them. Improved models will allow us to simulate various scenarios with greater confidence and avoid bugs, numerical errors or conceptual misunderstandings. This work will set a standard for future model development.

plant biology↗

bioRxiv2017

CRootBox: A Structural-Functional Modelling Framework For Root Systems

Background and AimsRoot architecture development determines the sites in soil where roots provide input of carbon and take up water and solutes. However, root architecture is difficult to determine experimentally when grown in opaque soil. Thus, root architectural models have been widely used and been further developed into functional-structural models that simulate the fate of water and solutes in the soil-root system. We present a root architectural model, CRootBox, as a flexible framework to model architecture and its interactions with static and dynamic soil environments.\n\nMethodsCRootBox is a C++ -based root architecture model with Python binding, so that CRootBox can be included via a shared library into any Python code. Output formats include VTP, DGF, RSML and CSV. We further created a database of published root architectural parameters. The capabilities of CRootBox for the unconfined growth of single root systems, as well as the different parameter sets, are highlighted into a freely available web application.\n\nKey resultsWe demonstrate the use of CRootBox for 5 different cases (1) free growth of individual root systems (2) growth of root systems in containers as a way to mimic experimental setups, (3), field scale simulation, (4) root growth as affected by heterogeneous, static soil conditions, and (5) coupling CRootBox with Soil Physics with Python code to dynamically compute water flow in soil, root water uptake, and water flow inside roots.\n\nConclusionsIn conclusion, we present a fast and flexible functional-structural root model which is based on state-of-the-art computational science methods. Its aim is to facilitate modelling of root responses to environmental conditions as well as the impact of root on soil. In the future, we plan to extend this approach to the aboveground part of the plant.

plant biology↗