Ronan Trépos

Using numerical plant models and phenotypic correlation space to design achievable ideotypes: Phenotypic correlations in ideotype design

Simulation models can be used to predict the outcome of plant traits modifications resulting from the genetic variation (and its interaction with the environment) on plant performance, hence gaining momentum in plant breeding process. Optimization methods complement those models in finding ideal values of a set of plant traits, maximizing a defined criteria (e.g. crop yield, light interception). However, using such methods carelessly may lead to misleading solutions, missing the appropriate traits or phenotypes. Therefore, we propose to use domains of potential phenotypes for the search of an optimum, taking into account correlations between traits to ground numerical experiments in biological reality. In addition, we propose a multi-objective optimization formulation using a metric of performance returned by numerical model and a metric of feasibility based on field observations. This can be solved with standard optimization algorithms without any model modification. We applied our approach to two contrasted simulation models: a process-based crop model of sunflower and a structural-functional plant model of apple tree. On both cases, we were able to characterize key plant traits and a continuum of optimal solutions, ranging from the most feasible to the most efficient. The present study thus provides a proof of concept for this approach and shows that it could improve trait-based breeding methods with paths describing desirable trait modifications both in direction and intensity.Numerical plant models can predict the outcome of plant traits modifications resulting from genetic variations, on plant performance, by simulating physiological processes and their interaction with the environment. Optimization methods complement those models to design ideotypes, that is, ideal values of a set of plant traits, resulting in optimal adaptation for given combinations of environment and management, mainly through the maximization of performance criteria (e.g. yield and light interception). As use of simulation models gains momentum in plant breeding, numerical experiments must be carefully engineered to provide accurate and attainable results, rooting them in biological reality. Here, we propose a multi-objective optimization formulation that includes a metric of performance, returned by the numerical model, and a metric of feasibility, accounting for correlations between traits based on field observations. We applied this approach to two contrasting models: a process-based crop model of sunflower and a functional-structural plant model of apple trees. In both cases, the method successfully characterized key plant traits and identified a continuum of optimal solutions, ranging from the most feasible to the most efficient. The present study thus provides successful proof of concept for this enhanced modelling approach, which identified paths for desirable trait modification, including direction and intensity.

Optimization of black-box models with uncertain climatic inputs—Application to sunflower ideotype design

Accounting for the interannual climatic variations is a well-known issue for simulation-based studies of environmental systems. It often requires intensive sampling (e.g., averaging the simulation outputs over many climatic series), which hinders many sequential processes, in particular optimization algorithms. We propose here an approach based on a subset selection in a large basis of climatic series, using an ad-hoc similarity function and clustering. A non-parametric reconstruction technique is introduced to estimate accurately the distribution of the output of interest using only the subset sampling. The proposed strategy is non-intrusive and generic (i.e. transposable to most models with climatic data inputs), and can be combined to most “off-the-shelf” optimization solvers. We apply our approach to sunflower ideotype design using the crop model SUNFLO. The underlying optimization problem is formulated as a multi-objective one to account for risk-aversion. Our approach achieves good performances even for limited computational budgets, outperforming significantly standard strategies.

A Package System for Maintaining Large Model Distributions in VLE Software

The Modeling and Simulation (M&S) is becoming a central activity in order to build, study and analyze new systems. To improve activities of M&S, we need to develop collaborative technologies. In this context, we develop the application software Virtual Laboratory Environment (VLE) to model, simulate and analyze dynamic systems. VLE is based on the Discrete Event System specification (DEVS) formalism, a widely recognized specification for modeling and simulating discrete events systems. The main features of the DEVS formalism are a modular and hierarchical approach of the M&S and a relative simplicity to develop the simulation algorithms. Researchers and engineers from different communities used VLE to develop and study models. However, the modelers need to share source code in order to reuse, couple and combine models. It is not sufficient because they are not helped for maintenance and version upgrades issues. In this paper we present a package system manager that greatly helps modelers to publish source code, binary code, exchange models, data and software application in VLE.

Decision in agroecosystems advanced modelling techniques studying global changes in environmental sciences

Abstract In recent years, the sustainable management of agricultural and ecological systems has become a major challenge. Sustainable management has to solve crucial environmental problems linked, in part, to rapid changes in context: climatic changes, agricultural policy objectives changes, and so on. Solving this challenge involves the joint development of research in modelling, simulation, and virtual experimentation. In this chapter, we present some recent work devoted to the modelling and simulation of complex systems involved in agroecosystem management. Then, we present new formalisms for management strategies design, based on the weighted constraint satisfaction problems or the Markov decision processes frameworks. We also show how simulation and conception of strategies can be integrated. Finally, we illustrate the use of the presented approaches on several case studies in agroecosystems management, jointly tackled with research teams in agronomy.

Sacadeau-Software, un logiciel d'aide à la décision pour améliorer la qualité de l'eau

Sacadeau-Software est un logiciel d’aide a la decision destine aux agronomes travaillant sur la pollution de l’eau dans les bassins versants et aux personnes en charge de la gestion de ces bassins versants. Cet outil se focalise sur la maitrise de la contamination des eaux par les pesticides apportes sur les cultures de mais. Il s’appuie sur un modele incluant la representation, d’une part, des processus biophysiques de transfert des pesticides a l’echelle d’un bassin versant et, d’autre part, des processus de decision dans le cadre de la culture du mais. Sacadeau-Software permet de lancer des simulations des cultures pour toutes les exploitations de l’ensemble d’un bassin versant et d’obtenir le taux de transfert des polluants a l’echelle du bassin versant. Des regles caracterisant les sous-parties du bassin versant ayant une pollution de l’eau a l’exutoire, et les sous-parties sans pollution, sont inferees automatiquement a partir des simulations effectuees. Un outil de visualisation permet alors de faire le lien entre les regles apprises et les exemples caracterises par ces regles. Enfin, un outil de recommandation d’actions propose, a partir des regles apprises, des actions propres a ameliorer une situation de pollution.