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Education and positions

  • 2011-now : Research engineer at INRAE (French National Institute for Agriculture, Food and Environment), in laboratory MIAT in Toulouse, France. I'm currently part of the RECORD platform team.
  • 2008-2011: Post doctoral position at INRA, MIAT in Toulouse, France
  • 2004-2008: PhD student at INRIA (French Institute for Research in Computer Science and Automation) in Rennes, France. Diploma obtained in 2008.
  • 2004-2004: Master of Science in Artificial Intelligence at University of Rennes, France. Diploma obtained in 2004
  • 2002-2004: Student at INSA, a French engineering school at Rennes, France. Diploma obtained in 2004

Research interest

I currently work on the simulation and the exploration of agro-environmental models. More precisely I rely on advanced techniques of modelisation to study agro-environmental systems, from the crop scale to the catchment area. To do this, I rely on sensitivity analysis, model inversion, bayesian estimation, data assimilation, meta-modeling and multi-objective optimization. I am also interested in formalisms for modelisation and simulation of complex systems, particularly in the DEVS (Discrete Event System Specification) framework.

Currently, I implement these techniques to study:

  • the impact of climatic data on crop models, using formal sensitivity analysis algorithms.
  • the question of ideotype conception by using a crop model based optimization. The goal is to conceive virtual crop varieties in specific conditions (soil, location, ...)
  • the assimilation of LAI (Leaf Area Index) observations based on remote sensing techniques, into a sunflower crop model.
  • the possibility to estimate soil water content by model inversion.

During my post-doctoral position (2008-2011), I worked on modelisation based design of cultural practices. Techniques involved where reinforcement learning (into a Markovian Decision Process framework) and global DIRECT optimization.

During my PhD (2004-2008), I worked on symbolic learning techniques (from the field of artificial intelligence) to provide optimized rules for decision support and to extract recurrent patterns from simulation results. The case study was the management of pesticide use in a little catchment area in Brittany, France, considering occurences of surface water contaminations. The techniques involved were essentially classification rules learning and inductive logic programming.


  • INRA Toulouse - MIAT unit (office C8 105), chemin de Borde-Rouge - BP 52627, 31326 Castanet-Tolosan Cedex, France.
  • tel : +33(0)
  • Mail: ronan[dot]trepos[at]inrae[dot]fr.


I'm part of the integrator teams of the following softwares:

  • VLE: a multi-modelling and simulation platform, coded in C++, based on the DEVS formalism. I develop modeling extensions (discrete-time models, ordinary differential equations), modeling techniques (Ensemble Kalman Filter, DIRECT global optimization, ...), graphical user interfaces (library QT) and ports to other languages (R, python).
  • WACS: a R package that provides multivariate weather generator for daily climate variables.


  • L. Fezzoua, M.Sc. internship (2019) on cattle phenotyping by solving inverse problem and simulation.
  • E. Alou-Telou, M.Sc. internship (2017) on bayesian estimation of water content using LAI observations and crop models.
  • Y. Fernandez, engineer temporary contract (2015-2016) on data assimilation techniques.
  • F. Boizard, M.Sc. internship (2015) on sensitivity analysis with climatic inputs: "Méthodes d'analyse de sensibilité de modèles pour entrées climatiques".
  • B. Poublan, M.Sc. internship (2014) on ideotype conception using model based optimization: "Optimisation de variétés de tournesol sous incertitude climatique".
  • P. Ithurralde, M.Sc. internship (2014) on stochastic generation of climatic data: "Génération stochastique de données météorologiques"
  • M. Garcia, B.Sc. internship (2013) on a R coding interface for sensitivity analysis: "Conception et développement informatique d'une interface sous R"
  • G. Bizouart, M.Sc. internship (2012) on meta modelisation techniques: "Méta-modélisation: Etat de l'art et application"

Courses taught

  • French research school on modelisation of agro-ecosystems: "Approches interdisciplinaires de la modélisation des agroécosystèmes" (2017)
  • Courses on Markov Decision processes. Master of Ecological Systems, University of Toulouse (2014 and 2015)
  • French research school on ideotype conception: "Conception d'idéotypes de plantes pour une agriculture durable" (2014)
  • Courses on Artificial Intelligence. ENSAR engineer school Rennes (2007)
  • Practical courses on Java programming. INSA engineer school Rennes (2006)


Book chapter

  • Quesnel, G. ; Akplogan, M. ; Bonneau, M. ; Martin-Clouaire, R. ; Dubois Peyrard, N. ; Rellier, J.-P. ; Sabbadin, R. ; Trépos, R (2015). Decision in agroecosystems advanced modelling techniques studying global changes in environmental sciences. Chapter in Developments in Environmental Modelling.
  • Faivre, R. ; Jeuffroy, M.-H. ; Monod, H. ; Trépos, R. (2014). Les méthodes génériques pour la conception d'idéotypes : apports des mathématiques appliquées. Conception d’idéotypes de plantes pour une agriculture durable (Debaeke P. et Quilot-Turion B., eds). Collection Ecole-chercheurs INRA, FormaSciences.
  • Cordier, M.O.; Aurousseau, P.; Falchier, M.; Garcia, F.; Gascuel-Odoux, C.; Heddadj, D.; Lebouille, L.; Masson, V.; Salmon-Monviola, J.; Tortrat, F. et Trépos, R. (2009). Modélisation du transfert d'herbicides dans un bassin versant dans le cadre d'un outil d'aide à la décision pour la maîtrise de la qualité des eaux. Concevoir et construire la décision: démarches en agriculture, agro-alimentaire et espace rurale. Editions Quae.

International journals

  • Ravier, C. ; Sabatier, R. ; Beillouin, D. ; Meynard, JM. ; Trépos, R. ; Jeuffroy, M-H. (2021) Decision rules for managing N fertilization based on model simulations and viability assessment. European Journal of Agronomy
  • Trépos, R. ; Champolivier, L. ; Dejoux, J-F. ; Al Bitar, A. ; Casadebaig, P. ; Debaeke, P.(2020). Forecasting Sunflower Grain Yield by Assimilating Leaf Area Index into a Crop Model. Remote Sensing
  • Beillouin, D. ; Trépos, R. ; Gauffreteau, A. ; Jeuffroy, M-H. (2018) Delayed and reduced nitrogen fertilization strategies decrease nitrogen losses while still achieving high yields and high grain quality in malting barley. European Journal of Agronomy
  • Beillouin, D. ; Leclère, M. ; M. Barbu, C. ; Bénézit, M. ; Trépos, R. ; Gauffreteau, A. ; Jeuffroy M-H. (2018) Azodyn-Barley, a winter-barley crop model for predicting and ranking genotypic yield, grain protein and grain size in contrasting pedoclimatic conditions. Agricultural and Forest Meteorology.
  • Picheny, V. ; Trépos, R. ; Casadebaig, P. (2017) Optimization of black-box models with uncertain climatic inputs. Application to sunflower ideotype design. PLOS One.
  • Picheny, V. ; Casadebaig, P. ; Trépos, R. ; Faivre, R. ; Da Silva, D. ; Vincourt, P. ; Costes, E. (2017) Using numerical plant models and phenotypic correlation space to design achievable ideotypes. Plant, Cell and Environment.
  • Trépos, R.; Salleb, A.; Cordier, M.-O.; Masson, V.; Gascuel-Odoux, C. (2013). Building Actions From Classification Rules. Knowledge and Information Systems.
  • Trépos, R.; Masson, V.; Cordier, M.-O.; Gascuel-Odoux, C.; Salmon-Monviola, J. (2012). Mining simulation data by rule induction to determine critical source areas of stream water pollution by herbicides. Computer and electronics in agriculture.
  • Salmon-Monviola, J. ; Gascuel, C. ; Garcia, F. ; Tortrat, F. ; Cordier, M.-O. ; Masson, V. ; Trépos, R. (2011) Simulating the effect of techniques and environmental constraints on the spatio-temporal distribution of herbicide applications and stream losses. Agriculture, Ecosystems and Environment.
  • Gascuel-Odoux, C.; Aurousseau, P.; Cordier, M.-O.; Durand, P.; Garcia, F.; Masson, V.; Salmon-Monviola, J.; Tortrat, F.; Trépos, R (2009). A decision-oriented model to evaluate the effect of land use and agricultural management on herbicide contamination in stream water. Environmental Modelling and Software.
  • Aurousseau, P.; Gascuel-Odoux, C.; Squividant, H.; Trépos, R.; Tortrat, F.; Cordier, M.-O. (2009). A plot drainage network as a conceptual tool for the spatialisation of surface flow pathways for agricultural catchments. Computers and Geosciences.

National journals

  • Champolivier, L. ; Debaeke, P.; Dejoux, J.F. ; Dizien, C.; Micheneau, A. ; Colombet, C. ; Gibrin, H.; Pontet, C.; Al Bitar, A. ; Trépos, R. ; Ansart, A. ; Marais Sicre, C.; Garric B. ; Mestries, E. ; Casadebaig, P. ; Fernandez-Diclo, Y. (2019) Outils, références et méthodes pour la construction d’un simulateur pour la prévision du rendement et de la qualité du tournesol à l’échelle territoriale mobilisant la télédétection satellitaire. Innovations Agronomiques. [1]
  • Quesnel, G. ; Akplogan, M. ; Bonneau, M. ; Martin-Clouaire, R. ; Peyrard, N. ; Rellier, J.-P. ; Sabbadin, R. ; Trépos, R. (2013) Décision dans les agro-écosystèmes. Revue d'Intelligence Artificielle.
  • Masson, V. ; Ployette, F. ; Cordier, M.-O. ; Gascuel, C. ; Trépos, R. (2013) Sacadeau-Software, un logiciel d'aide à la décision pour améliorer la qualité de l'eau. Revue d'Intelligence Artificielle.

International conferences

  • Peyrard, N. ; Sabbadin, R. ; Cros, MJ. ; Trépos, R. ; Nicol, S. (2020). A hidden semi-Markov model for inferring the structure of migratory bird flyway networks. ISEC 2020-International Statistical Ecology Conference
  • Wijmer, T.; Al-Bitar, A.; Rivalland, V.; Trépos, R.; Buis, S. (2019). Retrieval of soil water capacity at intra-plot scale using a data driven approach by combining unsupervised classification, crop modeling and Sentinel-2 remote sensing. Geophysical Research Abstracts
  • Micheneau, A.; Champolivier, L.; Dejoux, J.-F.; Al Bitar, A.; Trépos, R.; Casadebaig, P. ; Pontet, C.; Debaeke, P. (2018). Predicting sunflower grain yield using remote sensing data and models. ESA Congress [2]
  • Micheneau, A.; Champolivier, L.; Dejoux, J.-F.; Al Bitar, A.; Pontet, C.; Trépos, R.; Debaeke, P. (2017). Predicting sunflower grain yield using remote sensing data and statistical models. EFITA WCCA Congress [3]
  • Larmure, A. ; Benezit, M. ; Trépos, R. ; Lecomte, C. ; Jeuffroy, M-H. (2017) Azodyn-Pea, a crop model to adapt pea crop to climate change.Agriculture and Climate Change Conference
  • Casadebaig, P. ; Picheny, V. ; Trépos, R. ; Faivre, R. ; Da Silva, D. ; Vincourt, P. ; Costes, E. (2017). Using numerical plant models and phenotypic correlation space to design achievable ideotypes. Society of Experimental Biology Annual Main Meeting
  • Casadebaig, P. ; Poublan, B. ; Trépos, R. ; Picheny, V. ; Debaeke, P. (2015) Using plant phenotypic plasticity to improve crop performance and stability regarding climatic uncertainty: a computational study on sunflower. Procedia Environmental Sciences.
  • Gascuel-Odoux, C. ; Cordier, M-H. ; Grimaldi, C. ; Salmon-Monviola, J. ; Masson, V. ; Squividant, H. ; Trépos, R. (2013). A plot tree structure to represent surface flow connectivity in rural catchments: definition and application for mining critical source areas and temporal conditions. European Geosciences Union General Assembly
  • Trépos, R.; Raynal, H.; Quesnel, G. (2012). RECORD: an integrated platform for agro-ecosystems study. Simulation and Modeling Methodologies, Technologies and Applications.
  • Quesnel, G. ; Trépos, R. (2012). A package system for maintaining large model distributions in VLE software. Enabling Technologies: Infrastructure for Collaborative Enterprises.
  • Quesnel, G.; Trépos, R.; Chabrier, P.; Baudet J.; Duboz, R.; Ramat, E. (2011). Observations in DEVS framework. DEVS Integrative Modeling and Simulation Symposium.
  • Trépos, R.; Cordier, M.-O.; Gascuel-Odoux, C. and Masson, V (2008). Symbolic learning of relationships between agricultural activities and water quality from simulations for decision support. European Geosciences Union General Assembly.
  • Cordier, M.-O.; Garcia, F.; Gascuel-Odoux, C.; Masson, V.; Salleb, A.; Trépos, R. (2006). SACADEAU project: recommending actions from simulation results, BESAI (ECAI workshop on Binding Environmental Sciences and Artificial Intelligence).
  • Cordier, M.-O.; Garcia, F.; Gascuel-Odoux, C. ; Masson, V.; Salmon-Monviola, J.; Tortrat, F. et Trépos, R. (2005). A machine learning approach for evaluating the impact of land use and management practices on streamwater pollution by pesticides. Proceedings of International Congress on Modelling and Simulation.
  • Trépos, R.; Salleb, A.; Cordier, M.-O.; Masson, V.; Gascuel-Odoux, C. (2005). A Distance Based Approach for Action Recommendation. Proceedings of European Conference on Machine Learning.

National conferences

  • Trépos, R.; Lemarié, S.; Raynal, H.; Valantin-Morison, M; Couture, S. et Garcia, F. (2014). Apprentissage par renforcement pour l'optimisation de la conduite de culture du colza. Journées Francophones sur la Planification, la Décision et l'Apprentissage french pdf
  • Trépos, R.; Masson, V.; Cordier, M.-O. et Gascuel-Odoux, C. (2008). Induction de motifs spatiaux décrivant les chemins de ruissellement. Représentation et Raisonnement sur le Temps et l'Espace.
  • Trépos, R.; Cordier, M.-O.; Masson, V. et Gascuel-Odoux, C. (2007). Apprentissage de motifs spatiaux et agronomiques jouant un rôle dans la contamination de l'eau par les pesticides sur un bassin versant. 8ème Rencontres des Jeunes Chercheurs en Intelligence Artificielle.
  • Trépos, R.; Salleb, A.; Cordier, M.-O.; Masson, V.; Gascuel-Odoux,C. (2006). Une approche fondée sur une distance pour la recommandation d'actions. Reconnaissance des Formes et Intelligence Artificielle.

Technical reports

  • Casadebaig, P. ; Gauffreteau, A. ; Landré, A. ; Langlade, N. ; Mestries, E. ; Sarron, J. ; Trépos, R. ; Vincourt, P. ; Debaeke, P. (2020). Optimized cultivar deployment improves the efficiency and stability of sunflower crop production at national scale. bioRxiv
  • Picheny, V. ; Casadebaig, P. ; Trépos, R. ; Faivre, R. ; Da Silva, D. ; Vincourt, P. ; Costes, E. (2016) Finding realistic and efficient plant phenotypes using numerical models. arXiv preprint (arXiv:1603.03238)
  • Picheny, V. ; Trépos, R. ; Poublan, B. ; Casadebaig, P. (2015) Sunflower phenotype optimization under climatic uncertainties using crop models. arXiv preprint (arXiv:1509.05697)
  • Casadebaig, P. ; Trépos, R. ; Picheny, V. ; Langlade, N. B. ; Vincourt, P. ; Debaeke, P. (2014) Increased genetic diversity improves crop yield stability under climate variability: a computational study on sunflower. arXiv preprint (arXiv:1403.2825).
  • Salleb-Aouissi, A.; Trépos, R.; Cordier, M.-O. et Masson, V. (2008) From classification rules to action recommendation. Center for computational learning systems, New-York. Technical report CCLS-08-01.

Study reports

  • Trépos, R. (2008). Apprentissage symbolique à partir de données issues de simulation pour l'aide à la décision. Gestion d'un bassin versant pour une meilleure qualité de l'eau. Thèse à l'Université de Rennes 1 french content and english abstract
  • Trépos, R. (2004). Apprentissage d'automates pour la discrimination des classes de classification. INRIA-IRISA, projet SYMBIOSE. Rapport de Master Recherche.
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