Christophe Pradal

Drought is a significant factor in agricultural losses, making it imperative to understand how root system architecture and function (RSA) adapts to environmental conditions such as water deficit. HydroRoot is a functional-structural plant model (FSPM) aimed at analysing and simulating root system hydraulic and solute transport. The model provides a static hydraulic solver, a coupled water-solute transport solver, a statistical generator of RSA based on the Markov model, and a dynamic hydraulic model accounting for root growth. This paper presents the model, mathematical description of solver formalisms, and use cases with their associated tutorials. Four use cases illustrate the capabilities of HydroRoot, which has been successfully used for phenotyping root hydraulics across various species including Arabidopsis, maize, and pearl millet. The model-driven phenotyping method ‘Cut and Flow’ is presented to characterize root axial and radial conductivities on a given genotype. Finally, three step-by-step tutorials provide a structured way to learn how to use HydroRoot (i) to simulate hydraulics of a given root architecture, (ii) to simulate water and solute transport in a maize root, and (iii) to simulate hydraulics on two pearl millet genotypes in 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.

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 Beer’s 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 Beer’s law. ArchiCrop can support a range of applications, including crop model uncertainty analysis, model-assisted phenotyping, and ideotype design. Highlights: ArchiCrop is the first 3D+t botany-based parametric generative model for cereals. ArchiCrop downscales crop model dynamics to 3D+t architecture canopies efficiently. ArchiCrop compares big leaf versus leaf-resolved light interception. Plant architectures with same leaf area intercept light with up to 27% variability. ArchiCrop helps ideotyping, crop model evaluation and error propagation analysis. Graphical

La modélisation en biologie, plus particulièrement celle de la croissance et du fonctionnement des plantes, est un domaine actuellement en pleine expansion, utile pour appréhender les enjeux liés au changement climatique et à la sécurité alimentaire au niveau mondial. La modélisation et la simulation sont des outils incontournables pour la compréhension des relations complexes entre l'architecture des plantes et les processus qui influencent leur croissance dans un environnement changeant. Pour la modélisation des plantes, un grand nombre de formalismes ont été développés dans de nombreuses disciplines et à différentes échelles de représentation. L'objectif de cette thèse est de définir une architecture modulaire qui permette de simuler des systèmes structure-fonction en réutilisant et en assemblant différents modèles existants. Nous étudierons d'abord les différentes approches de la réutilisation logicielle, proposées par Krueger, les systèmes à tableau noir et les systèmes de workflows scientifiques. Ces différentes approches sont utilisées afin de faire coopérer, de réutiliser et d'assembler des artefacts logiciels de façon modulaire. A partir du constat que ces systèmes fournissent les abstractions nécessaires à l'intégration d'artefacts variés, notre hypothèse de travail est qu'une architecture hybride, basée sur les systèmes à tableau noir avec un contrôle procédural piloté par dataflow, permettrait à la fois d'obtenir la modularité tout en permettant au modélisateur de garder le contrôle sur l'exécution. Dans le chapitre 2, nous décrivons la plateforme OpenAlea, une plateforme à composants logiciels et offrant un système de workflow scientifique, permettant l'assemblage et la composition de modèles à travers une interface de programmation visuelle. Dans le chapitre 3, nous proposons une structure de données pour le tableau noir, associant une représentation topologique de l'architecture des plantes à différentes échelles, le Multiscale Tree Graph, et sa spatialisation géométrique à l'aide de la bibliothèque 3D PlantGL. Ensuite, dans le chapitre 4, nous présentons les lambda-dataflows, une extension des dataflows permettant de coupler simulation et analyse. Puis, dans le chapitre 5, nous présentons une première application, qui illustre l'utilisation d'un modèle générique de feuilles de graminées dans différents modèles de plantes. Finalement, dans le chapitre 6, nous présentons l'ensemble des éléments de l'architecture utilisés pour élaborer un cadre générique de modélisation du développement des maladies foliaires dans un couvert architecturé. L'architecture présentée dans cette thèse et sa mise en œuvre dans OpenAlea sont un premier pas vers la réalisation de plateformes de modélisation intégratives ouvertes, permettant la coopération de modèles hétérogènes en biologie. L'utilisation du formalisme de workflows scientifiques en analyse et en simulation permet notamment d'envisager à court terme l'élaboration des plateformes de simulation collaboratives et distribuées à grande échelle.

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.

Decision support systems (DSS) in crop protection provide valuable support for pest risk prognosis and recommendations for pest control, enabling farmers to make better-informed decisions. As a part of the European Union’s strategy for the sustainable use of plant protection products, the “IPM Decisions” project developed an online platform that gives farmers and advisors access to a wide range of DSS for major pests, weeds, and diseases in a variety of crops across Europe. Multiple DSS models relevant for different crops and geographical regions of Europe were selected for integration in the platform. Information on the models is compiled into a model catalogue, which serves as a core component of the IPM Decisions platform. To facilitate the use of these models, two application programming interfaces (APIs) were developed. In line with the FAIR (findable, accessible, interoperable, and reusable) principles, the DSS API provides access to models and their metadata, including descriptions of input and output parameters. The weather API enables access to European online weather data sources and adapts this data to meet the requirements of DSS models. While these APIs are integrated into the IPM decisions platform, they are also open source, allowing other crop protection and farm management software to inspect, download, modify, install, run, and use them. In this article, we describe the development of the DSS and weather APIs, outline their structure and definitions, and present the services that DSS API and weather API provide. Finally, we demonstrate their application through three practical use cases.

Net nitrogen (N) uptake results from active and water-mediated N inflows, partly offset by N exudation. These processes exhibit significant variations along root axes, suggesting the existence of specific zones of higher N exchanges. However, the precise location, drivers, and significance of such active root zones in root N budget remain unclear. Here, we identified and characterized active root zones using Root-CyNAPS, a new functional-structural plant model that simulates the space-time variations in net N uptake, based on the interactions between nitrogen, carbon, and water flows, and root anatomy in each segment of a 3D root system architecture. Our simulations on wheat for various plant ages and external nitrate concentrations revealed two zones of preferential net N uptake: one near the root apices and the other one coinciding with the lateral root initiation zone, both characterized by a net N uptake activity about 10 times higher than adjacent segments. Higher water uptake and N exudation rates were also located next to apices. The contribution of most active root zones (defined here by the top 10% of root length) varied between 20% and 80\% of plant net N uptake, depending on root environment and age. Our simulations also showed that water-mediated N uptake could represent up to two thirds of gross N uptake, while N losses could offset more than half of it. Root-CyNAPS offers coupling opportunities with other functional-structural models to simulate nitrogen, carbon, and water multiscale interactions across the soil-plant-atmosphere continuum.

Crop protection and pest management are major economic and environmental concerns throughout Europe. The consultation of decision support systems (DSS) to guide decisions relating to Integrated Pest Management (IPM) is one of the key principles of IPM, reducing the ambiguity around potential risks to crop health. ‘Pests’ in this context include invertebrate pests, weeds and pathogens. The impact of DSS can be limited by a lack of awareness of DSS availability, inconsistencies in the user functions of different DSS, regional fragmentation of access, and a lack of transparency of the origin, validity, and benefits of DSS. Failure to address these limitations undermines trust in IPM DSS and leads to a reluctance of farmers and advisors to invest time in consulting multiple DSS sources as part of their agronomic decision toolbox. The EU-funded IPM Decisions project (Grant agreement ID: 817617) addressed these limitations by creating a Europe-wide free-access online platform. The IPM Decisions platform was designed in consultation with farmers, advisors and wider stakeholders to increase access to and uptake of IPM DSS integrated within it. It offers an end-point for IPM researchers and DSS developers to make adapted and novel DSS available to users, and provides a ‘one-stop shop' for farmers and advisors looking to consult free access or paid IPM DSS. Dedicated dashboards within the platform facilitate farm set up, consultation of DSS, comparison of DSS outputs, and adjustment of model parameters for adaption to different pests/regions. The IPM Decisions digital infrastructure enables easy integration of models and data with external platforms, providing a framework for accessing and sharing models and data between researchers and developers. The platform therefore provides both a ready to go user interface for new DSS, as well as the infrastructure to support and connect existing and future user interfaces.

Background and Aims: 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 plant-soil interactions. However, its spatial and temporal dynamics remain poorly understood. Methods: 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. Results: 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. Conclusion: 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.

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.