Publications

2017

Gauchi J.-P. , Bensadoun A., Colas F., Colbach N, 2017. Metamodeling and global sensitivity analysis for computer models
with correlated inputs: A practical approach tested with a 3D light interception computer model. Environmental Modelling & Software, 92, 40-56.

Saccareau M., Moreno C.R., Kyriazakis I., Faivre R.,  Bishop S.C., 2016. Modelling gastrointestinal parasitism evolution in a sheep flock over two reproductive seasons: in silico exploration and sensitivity analysis. Parasitology, Vol 143, Issue 12, pp. 1509-1531 : online.

Picheny V., Casadebaig P., Trépos R., Faivre R., Da Silva D., Vincourt P., Costes E., 2017. Finding realistic and efficient plant phenotypes using numerical models. Plant, Cell and Environment,DOI : 10.1111/pce.13001 (arxiv )

2016

Casadebaig P.,  Zheng B.,  Chapman S.,  Huth N.,  Faivre R., Chenu K. 2016. Assessment of the potential impacts of plant traits across environments by combining global sensitivity analysis and dynamic modeling in wheat. http://dx.doi.org/doi:10.1371/journal.pone.0146385

2015

Wang J., Faivre R., Richard H. and Monod H., 2015. mtk: A General-Purpose and Extensible R Environment for Uncertainty and Sensitivity Analyses of Numerical Experiments. https://journal.r-project.org/archive/2015-2/, 206-226.

2014

Da Silva D., Han L., Faivre R., Costes E., 2014. Influence of the variation of geometrical and topological traits on light interception efficiency of apple trees: sensitivity analysis and metamodelling for ideotype definition. Annals of Botany 114: 739–752, 2014. doi: 10.1093/aob/mcu034

Faivre R., Jeuffroy M.-H., Monod H., Trépos R., 2014. Les méthodes génériques pour la conception d'idéotypes : apports des mathématiques appliquées. In: Philippe Debaeke, Bénédicte Quilot-Turion, dir.,  Conception d’idéotypes de plantes pour une agriculture durable.  Ecole-chercheurs INRA, FormaSciences, FPN, INRA-CIRAD (ISBN 2-7380-1347-3), pp 185-217.

2013

Jabot F.,  Faure T., Dumoulin N. (2013). EasyABC: Performing efficient approximate Bayesian computation sampling schemes using R. Methods in Ecology and Evolution 4 (7), 20. (doi)

Carpani M., Bergez J.-E., Monod H. (2011). Sensitivity analysis of a hierarchical qualitative model for sustainability assessment of cropping systems - the case of MASC. Environmental Modelling & Software, 27-28, 15-22. (doi)

Courcoul A., Monod H., Nielen M., Klinkenberg D., Hogerwerf L., Beaudeau F., Vergu E. (2011). Modelling the effect of heterogeneity of shedding on the within herd Coxiella burnetii spread and identification of key parameters by sensitivity analysis. Journal of Theoretical Biology, 284, 130-141. (doi)

Lamboni M., Monod H., Makowski D. (2011). Multivariate sensitivity analysis to measure global contribution of input factors in dynamic models. Reliability Engineering and System Safety, 96: 450-459. (doi)

Lamboni M., Makowski D., Monod H. (2011). Indices de sensibilité, sélection de paramètres et erreur quadratique de prédiction: des liaisons dangereuses? Journal de la Société Française de Statistique, 152, 26-48.

2010

Ellouze M., Gauchi J.P., Augustin J. (2010). Global sensitivity analysis applied to a contamination assessment model of listeria monocytogenes in cold smoked salmon at consumption. Risk Analysis, 30(5): 841-852.
 

Ellouze M., Gauchi J.P., Augustin J. (2010). Use of global sensitivity analysis in quantitative microbial risk assessment: application to the evaluation of a biological time temperature integrator as a quality and safety indicator for cold smoked salmon. Journal Food Microbiology, 2010. on line.

Lehuta, S., Mahévas, S., Petitgas, P. et Pelletier, D., 2010. 'Combining sensitivity and uncertainty analysis to evaluate the impact of management measures with ISIS–Fish: marine protected areas for the Bay of Biscay anchovy(Engraulis encrasicolus) fishery. ICES journal of Marine science67:1063-1075.

2009

Lamboni M., Makowski D., Lehuger S., Gabrielle B., Monod H. (2009). Multivariate global sensitivity analysis for dynamic crop models. Fields Crop Research, Vol. 113, pp. 312-320. (doi)

Lurette, A., Touzeau, S., Lamboni, M., Monod, H. (2009). Sensitivity analysis to identify key parameters influencing Salmonella infection dynamics in a pig batch. J. Theor. Biol., Vol. 258(1), pp. 43-52. (doi)

2008

Viaud, V., Monod, H., Lavigne, C., Angevin, F., Adamczyk, K. (2008). Spatial sensitivity of maize gene-flow to landscape pattern: a simulation approach. Landscape Ecology, Vol. 23, pp. 1067-1079. (doi)
 

2007

Ginot, V., Monod, H. (2007). Exploring Models by Simulation. In: Amblard, F., Phan, D. (eds.) Agent-based modelling and simulation in the social and human sciences, Chapter 3, pp. 63-91. Bardwell Press.

2006

Ginot, V., Monod, H., 2006. Explorer les modèles par simulation: application aux analyses de sensibilité. In: Amblard, F., Phan, D. (eds.) Modélisation et Simulation Multi agents, applications pour les Sciences
de l'Homme et de la Société, Chapitre 3, pp. 75-100. Lavoisier (Hermès Science), Paris.
 

Ginot, V., Gaba, S., Beaudouin, R., Aries, F., Monod, H. (2006). Combined use of local and ANOVA-based global sensitivity analyses for the investigation of a stochastic dynamic model: Application to the case study of an individual-based model of a fish population. Ecological Modelling, 193, pp. 479-491.

Makowski, D., Naud, C., Monod, H., Jeuffroy, M.-H., Barbottin, A. (2006). Global sensitivity analysis for calculating the contribution of genetic parameters to the variance of crop model prediction. Reliability Engineering and System Safety, Vol. 91(10), pp. 1142-1147.
 

Monod, H., Naud, C. Makowski, D., 2006. Uncertainty and sensitivity analysis for crop models. In: Wallach, D., Makowski, D., Jones, J. W. (eds.) Working with Dynamic Crop Models: Evaluation, Analysis, Parameterization, and Applications, Chapter 4. Elsevier, pp. 55-100.