M. Arulampalam, S. Maskell, T. Gordon, and N. Clapp, A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking, IEEE Transactions on Signal Processing, vol.50, issue.2, pp.174-188, 2002.
DOI : 10.1109/78.978374

C. Baey, A. Didier, S. Li, S. Lemaire, F. Maupas et al., Evaluation of the predictive capacity of five plant growth models for sugar beet, 2012 IEEE 4th International Symposium on Plant Growth Modeling, Simulation, Visualization and Applications, 2012.
DOI : 10.1109/PMA.2012.6524809
URL : https://hal.archives-ouvertes.fr/hal-00776389

J. G. Booth and J. P. Hobert, Maximizing generalized linear mixed model likelihoods with an automated Monte Carlo EM algorithm, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.61, issue.1, pp.61-265, 1999.
DOI : 10.1111/1467-9868.00176

F. Campillo and V. Rossi, Convolution Particle Filter for Parameter Estimation in General State-Space Models, IEEE Transactions on Aerospace and Electronic Systems, vol.45, issue.3, pp.1063-1072, 2009.
DOI : 10.1109/TAES.2009.5259183

F. Campolongo, J. Cariboni, and A. Saltelli, An effective screening design for sensitivity analysis of large models, Environmental Modelling and Software, pp.1509-1518, 2007.

. Cournède, Filtrage par noyaux de convolution itératif, Actes des 44èmes Journées de Statistique (JdS'12), 2012.

Y. Chen and P. Cournède, Assessment of parameter uncertainty in plant growth model identification, 2012 IEEE 4th International Symposium on Plant Growth Modeling, Simulation, Visualization and Applications, 2012.
DOI : 10.1109/PMA.2012.6524817
URL : https://hal.archives-ouvertes.fr/hal-00776551

P. Cournède, Y. Chen, Q. Wu, C. Baey, and B. Bayol, Development and Evaluation of Plant Growth Models: Methodology and Implementation in the PYGMALION platform, Mathematical Modelling of Natural Phenomena, vol.8, issue.4, 2013.
DOI : 10.1051/mmnp/20138407

A. Doucet, N. D. Freitas, and N. Gordon, Sequential Monte Carlo methods in practice, 2001.
DOI : 10.1007/978-1-4757-3437-9

B. Efron and R. Tibshirani, An Introduction to the Bootstrap, CRC Monographs on Statistics and Applied Probability, 1994.
DOI : 10.1007/978-1-4899-4541-9

G. Evensen, Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics, Journal of Geophysical Research, vol.109, issue.Part 4, pp.10143-10162, 1994.
DOI : 10.1029/94JC00572

]. N. Gordon, D. Salmond, and A. Smith, Data assimilation: The ensemble Kalman Filter Novel approach to nonlinear/non-gaussian bayesian state estimation, Proc. Inst. Electr. Eng., Part F, pp.140-107, 1993.

V. Houì-es, B. Mary, M. Guérif, D. Makowski, and E. Justes, Evaluation of the ability of the crop model STICS to recommend nitrogen fertilisation rates according to agro-environmental criteria, Agronomie, pp.24-339, 2004.

A. Jazwinski, Stochastic Processes and Filtering Theory, 1970.

N. Kantas, A. Doucet, S. S. Singh, and J. M. Maciejowski, An overview of sequential monte carlo methods for parameter estimation in general statespace models, Proceedings of the 15th IFAC Symposium on System Identification, 2009.

G. Kitagawa, Monte carlo filter and smoother for non-gaussian nonlinear state space models, Journal of Computational and Graphical Statistics, vol.5, pp.1-25, 1996.

M. Launay and M. Guérif, Assimilating remote sensing data into a crop model to improve predictive performance for spatial applications, Agriculture, Ecosystems & Environment, vol.111, issue.1-4, pp.321-339, 2005.
DOI : 10.1016/j.agee.2005.06.005

S. Lemaire, F. Maupas, P. Cournède, and P. De-reffye, A morphogenetic crop model for sugarbeet (beta vulgaris l.), International Symposium on Crop Modeling and Decision Support: ISCMDS, 2008.
URL : https://hal.archives-ouvertes.fr/inria-00336415

J. Monteith, Climate and the Efficiency of Crop Production in Britain [and Discussion], Philosophical Transactions of the Royal Society B: Biological Sciences, vol.281, issue.980, pp.277-294, 1977.
DOI : 10.1098/rstb.1977.0140

C. Naud, D. Makowski, and M. Jeuffroy, Is it useful to combine measurements taken during the growing season with a dynamic model to predict the nitrogen status of winter wheat?, European Journal of Agronomy, vol.28, issue.3, pp.291-300, 2008.
DOI : 10.1016/j.eja.2007.08.005
URL : https://hal.archives-ouvertes.fr/hal-01173164

N. Oudjane and C. Musso, Regularized particle schemes applied to the tracking problem, International Radar Symposium, 1998.

R. H. Reichle, P. W. Jeffrey, D. K. Randal, and R. H. Paul, Extended versus Ensemble Kalman Filtering for Land Data Assimilation, Journal of Hydrometeorology, vol.3, issue.6, pp.728-740, 2002.
DOI : 10.1175/1525-7541(2002)003<0728:EVEKFF>2.0.CO;2

P. Van-leeuwen and G. Evensen, Data Assimilation and Inverse Methods in Terms of a Probabilistic Formulation, Monthly Weather Review, vol.124, issue.12, pp.2898-2913, 1996.
DOI : 10.1175/1520-0493(1996)124<2898:DAAIMI>2.0.CO;2

Q. Wu, P. Cournède, and A. Mathieu, An efficient computational method for global sensitivity analysis and its application to tree growth modelling, Reliability Engineering & System Safety, vol.107, pp.35-43, 2012.
DOI : 10.1016/j.ress.2011.07.001
URL : https://hal.archives-ouvertes.fr/hal-00639539