Biology subjects

Pradal, C.

Publications and source records attributed to Pradal, C..

9 recordsLinked to original sources

bioRxiv2026

ArchiCrop: a 3D+t architectural model driven by crop model dynamics

Evolving agricultural practices and contexts invite to reconsider the way crop and plant models represent agroecosystem processes. Crop models assume spatial homogeneity, which reduces confidence in their predictions for structurally heterogeneous systems, whereas FSPMs complexity limits their application to field-scale or large-scale studies. To benefit from strengths of both approaches, we introduce ArchiCrop, a parametric 3D architectural model of cereals that generates plant geometries constrained by crop model dynamics and coordination rules. Inspired by the concept of equifinality, ArchiCrop generates a morphospace of architecturally diverse morphotypes which remain equivalent at crop scale in terms of LAI and height. This multiscale approach enables the comparison of processes computed at different scales on wheat, rice, maize and sorghum. We demonstrate its application evaluating light interception Beers formalism in STICS soil-crop model relying on the leaf-resolved radiosity model Caribu for the 3D reference simulations, for a sorghum monocrop. We show that the consideration of the variability of only two plant architectural traits, leaf insertion angle and leaf number, introduces up to 27% of uncertainty in the cumulated absorbed light at the end of the season. A possible outcome from this method is also the definition of metamodels for crop model processes, as exemplified for extinction coefficient of Beers law. ArchiCrop can support a range of applications, including crop model uncertainty analysis, model-assisted phenotyping, and ideotype design. HighlightsO_LIArchiCrop is the first 3D+t botany-based parametric generative model for cereals. C_LIO_LIArchiCrop downscales crop model dynamics to 3D+t architecture canopies efficiently. C_LIO_LIArchiCrop compares big leaf versus leaf-resolved light interception. C_LIO_LIPlant architectures with same leaf area intercept light with up to 27% variability. C_LIO_LIArchiCrop helps ideotyping, crop model evaluation and error propagation analysis. C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=105 SRC="FIGDIR/small/716970v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@6534b3org.highwire.dtl.DTLVardef@66df41org.highwire.dtl.DTLVardef@1cb4c9dorg.highwire.dtl.DTLVardef@12fa30_HPS_FORMAT_FIGEXP M_FIG C_FIG

plant biology↗

bioRxiv2026

OpenAlea.HydroRoot: A modelling framework to dissect, predict and phenotype branched root hydraulic architecture

Drought is a significant factor in agricultural losses, making it imperative to understand how root system architecture (RSA) adapts to environmental condition like water deficit. HydroRoot is a functional-structural plant model (FSPM) aimed at analyzing and simulating hydraulic and solute transport of RSA. The model integrates a static hydraulic solver, a coupled water-solute transport solver, a statistical generator of RSA based on Markov model, and a dynamic hydraulic model accounting for root growth. This paper presents the model, the mathematical description of the formalism of solvers, and use cases with their associated tutorials. Five use cases illustrate capabilities of HydroRoot, which has been successfully used for phenotyping root hydraulics across various species, including Arabidopsis, maize, and millet. The model-driven phenotyping method "cut and flow" is presented to characterize axial and radial conductivities on a given root genotype. Finally, three step-by-step tutorials provide a structured way to learn how to use HydroRoot 1) to simulate hydraulic on a given architecture, 2) to simulate water and solute transport on a maize root, and 3) to simulate hydraulic on two pearl millet genotypes with varying soil conditions. Hydroroot is an open-source package of the OpenAlea platform, with the code publicly available on Github. A comprehensive documentation is available with a reproducible gallery of examples.

plant biology↗

bioRxiv2025

Growth Cost and Transport Efficiency Tradeoffs Define Root System Optimization Across Varying Developmental Stages and Environments in Arabidopsis

Root system architecture (RSA) is central to plant adaptation and fitness, yet the design principles and regulatory mechanisms connecting RSA to environmental adaptation are not well understood. We developed Ariadne, a semi-automated software for quantifying cost-efficiency tradeoffs of RSA by mapping root networks onto a Pareto-optimality framework, which describes the balance between resource transport efficiency and construction cost. Applying Ariadne to Arabidopsis thaliana, we found that root architectures consistently assume Pareto-optimal forms across developmental stages, genotypes, and environmental conditions. Using the Discovery Engine, an engine that combines machine learning together with interpretability techniques, we found developmental stage, the hy5/chl1-5 genotype, and manganese availability as important determinants of the cost-efficiency tradeoff, with manganese exerting a unique influence not observed for other nutrients. These results reveal that RSA plasticity is genetically constrained to cost-efficiency optimal configurations and that developmental and environmental factors shift RSA on the pareto front, with manganese acting as a strong modulator of the transport efficiency and construction cost balance.

plant biology↗

bioRxiv2025

Deciphering spatiotemporal patterns of rhizodeposition with a functional-structural root model: RhizoDep

Rhizodeposition, i.e. the release of organic matters by roots, constitutes a significant fraction of the plant carbon (C) budget and plays a key role in soil-plant interactions. However, its spatial and temporal dynamics remain poorly understood. We developed RhizoDep, a new functional-structural root model that simulates 3D root growth, respiration, and rhizodeposition based on C balance and root morphology at the individual root segment level. Our model successfully reproduced the dynamics of belowground C flows observed in a previous pulse-labelling field experiment on spring wheat. Our simulations revealed that root C exudation largely dominated over mucilage secretion and cap cells sloughing in terms of C release. The spatial distribution of exudation rate along the roots was driven by the preferential unloading of sugars to support root elongation and emergence, and was modulated by the formation of apoplastic barriers. Furthermore, our results demonstrated that, for a given C allocation flow to roots, variations in root hairs or lateral root number had minimal effects on rhizodeposition, whereas changes in root tissue density had a significant impact. RhizoDep offers a new opportunity to explore the dynamics of C exchange at the plant-soil interface and to identify traits and environmental conditions that favor rhizodeposition. HighlightUsing the new model RhizoDep, we simulated distinct spatial and temporal patterns of rhizodeposition along the roots of spring wheat over its complete growth cycle, and identified their main drivers. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=82 SRC="FIGDIR/small/646173v1_ufig1.gif" ALT="Figure 1"> View larger version (34K): org.highwire.dtl.DTLVardef@155786borg.highwire.dtl.DTLVardef@54c1b1org.highwire.dtl.DTLVardef@784ca9org.highwire.dtl.DTLVardef@1f0f09d_HPS_FORMAT_FIGEXP M_FIG C_FIG

physiology↗

bioRxiv2022

PhenoTrack3D: an automatic high-throughput phenotyping pipeline to track maize organs over time

BackgroundHigh-throughput phenotyping platforms allow the study of the form and function of a large number of genotypes subjected to different growing conditions (GxE). A number of image acquisition and processing pipelines have been developed to automate this process, for micro-plots in the field and for individual plants in controlled conditions. Capturing shoot development requires extracting from images both the evolution of the 3D plant architecture as a whole, and a temporal tracking of the growth of its organs. ResultsWe propose PhenoTrack3D, a new pipeline to extract a 3D+t reconstruction of maize at organ level from plant images. It allows the study of plant architecture and individual organ development over time during the entire growth cycle. PhenoTrack3D improves a former method limited to 3D reconstruction at a single time point [Artzet et al., 2019] by (i) a novel stem detection method based on deep-learning and (ii) a new and original multiple sequence alignment method to perform the temporal tracking of ligulated leaves. Our method exploits both the consistent geometry of ligulated leaves over time and the unambiguous topology of the stem axis. Growing leaves are tracked afterwards with a distance-based approach. This pipeline is validated on a challenging dataset of 60 maize hybrids imaged daily from emergence to maturity in the PhenoArch platform (ca. 250,000 images). Stem tip was precisely detected over time (RMSE < 2.1cm). 97.7% and 85.3% of ligulated and growing leaves respectively were assigned to the correct rank after tracking, on 30 plants x 43 dates. The pipeline allowed to extract various development and architecture traits at organ level, with good correlation to manual observations overall, on random subsets of 10 to 355 plants. ConclusionsWe developed a novel phenotyping method based on sequence alignment and deep-learning. It allows to characterise automatically and at a high-throughput the development of maize architecture at organ level. It has been validated for hundreds of plants during the entire development cycle, showing its applicability to the GxE analyses of large maize datasets.

plant biology↗

bioRxiv2022

High-throughput and automatic structural and developmental root phenotyping on Arabidopsis seedlings

BackgroundHigh-throughput phenotyping is crucial for the genetic and molecular understanding of adaptive root system development. In recent years, imaging automata have been developed to acquire the root system architecture of many genotypes grown in Petri dishes to explore the Genetic x Environment (GxE) interaction. There is now an increasing interest in understanding the dynamics of the adaptive responses, such as the organ apparition or the growth rate. However, due to the increasing complexity of root architectures in development, the accurate description of the topology, geometry, and dynamics of a growing root system remains a challenge. ResultsWe designed a high-throughput phenotyping method, combining an imaging device and an automatic analysis pipeline based on registration and topological tracking, capable of accurately describing the topology and geometry of observed root systems in 2D+t. The method was tested on a challenging Arabidopsis seedling dataset, including numerous root occlusions and crossovers. Static phenes are estimated with high accuracy (R2 = 0.996 and 0, 923 for primary and second-order roots length, respectively). These performances are similar to state-of-the-art results obtained on root systems of equal or lower complexity. In addition, our pipeline estimates dynamic phenes accurately between two successive observations (R2 = 0. 938 for lateral root growth). ConclusionsWe designed a novel method of root tracking that accurately and automatically measures both static and dynamic RSA parameters from a novel high-throughput root phenotyping platform. It has been used to characterize developing patterns of root systems grown under various environmental conditions. It provides a solid basis to explore the GxE interaction controlling the dynamics of root system architecture adaptive responses. In future work, our approach will be adapted to a wider range of imaging configurations and species.

plant biology↗

bioRxiv2019

Phenomenal: An automatic open source library for 3D shoot architecture reconstruction and analysis for image-based plant phenotyping

In the era of high-throughput visual plant phenotyping, it is crucial to design fully automated and flexible workflows able to derive quantitative traits from plant images. Over the last years, several software supports the extraction of architectural features of shoot systems. Yet currently no end-to-end systems are able to extract both 3D shoot topology and geometry of plants automatically from images on large datasets and a large range of species. In particular, these software essentially deal with dicotyledons, whose architecture is comparatively easier to analyze than monocotyledons. To tackle these challenges, we designed the Phenomenal software featured with: (i) a completely automatic workflow system including data import, reconstruction of 3D plant architecture for a range of species and quantitative measurements on the reconstructed plants; (ii) an open source library for the development and comparison of new algorithms to perform 3D shoot reconstruction and (iii) an integration framework to couple workflow outputs with existing models towards model-assisted phenotyping. Phenomenal analyzes a large variety of data sets and species from images of high-throughput phenotyping platform experiments to published data obtained in different conditions and provided in a different format. Phenomenal has been validated both on manual measurements and synthetic data simulated by 3D models. It has been also tested on other published datasets to reproduce a published semi-automatic reconstruction workflow in an automatic way. Phenomenal is available as an open-source software on a public repository.

plant biology↗

bioRxiv2019

HydroShoot: a functional-structural plant model for simulating hydraulic structure, gas and energy exchange dynamics of complex plant canopies under water deficit - application to grapevine (Vitis vinifera L.)

This paper aims at presenting HydroShoot, a functional-structural plant model (FSPM) that is developed to simulate gas-exchange rates of complex plant canopies under water deficit conditions, by scaling up gas-exchange rates from the leaf to the canopy levels. The main hypothesis is that simulating both the hydraulic structure of the shoot together with the energy budget of individual leaves is the asset for successfully achieving this up-scaling task. HydroShoot was hence built as the ensemble of three interacting modules: hydraulic which calculates the distribution of xylem water potential across shoot hydraulic segments, energy which calculates the complete energy budget of individual leaves, and exchange which calculates net assimilation and transpiration rates of individual leaves. HydroShoot was coupled with irradiance interception and soil water balance models, and was evaluated on virtual and real grapevines having strongly contrasted canopies, under well-watered and water-deficit conditions. HydroShoot captured accurately the impact of canopy architecture and the varying soil water deficit conditions on plant-scale gas-exchange rates and leaf-scale temperature and water potential distributions. Both shoot hydraulic structure and leaf energy budget simulations were, as postulated, required to adequately scaling-up leaf to canopy gas-exchange rates. Notwithstanding, simulating the hydraulic structure of the shoot was found far more necessary to adequately performing this scaling task than simulating leaf energy balance. That is, the intra-canopy variability of leaf water potential was a better predictor of the reduction of whole plant gas-exchange rates under water deficit than the intra-canopy variability of leaf temperature. We conclude therefore that simulating the shoot hydraulic structure is a prerequisite if FSPM's are to be used to assess gas-exchange rates of complex plant canopies as those of grapevines. Finally HydroShoot is available through the OpenAlea platform (https://github.com/openalea/hydroshoot) as a set of reusable modules.

plant biology↗

bioRxiv2018

MuSCA: a multi-scale model to explore carbon allocation in plants

Background and aimsCarbon allocation in plants is usually represented at a specific spatial scale, peculiar to each model. This makes the results obtained by different models, and the impact of their scale of representation, difficult to compare. In this work we developed a Multi Scale Carbon Allocation model (MuSCA) that can be applied at different, user-defined, topological scales of a plant, and used to assess the impact of each spatial scale on simulated results and computation time.\n\nMethodsModel multi-scale consistency and behavior were tested by applications on three realistic apple tree structures. Carbon allocation was computed at five spatial scales, spanning from the metamer (the finest scale, used as a reference) up to 1st order branches, and for different values of a sap friction coefficient. Fruit dry mass increments were compared across spatial scales and with field data.\n\nKey ResultsThe model showed physiological coherence in representing competition for carbon assimilates. Results from intermediate values of the friction parameter best fitted the field data. For these, fruit growth simulated at the metamer scale (considered as a reference) differed from about 1% at growth unit scale up to 35% at first order branch scale. Generally, the coarser the spatial scale the more fruit growth diverged from the reference and the lower the obtained within-tree fruit growth variability. Coherence in the carbon allocated across scales was also differently impacted, depending on the tree structure considered. Decreasing the topological resolution reduced computation time up to four orders of magnitude.\n\nConclusionsMuSCA revealed that the topological scale has a major influence on the simulation of carbon allocation, suggesting that this factor should be carefully evaluated when using different carbon allocation models or comparing their results. Trades-off between computation time and prediction accuracy can be evaluated by changing topological scales.

plant biology↗