Y. Chen and P. Cournède, Data assimilation to reduce uncertainty of crop yield prediction based on the Log-normal allocation and senescence crop model and convolution particle filtering, Ecological Modelling. Accepted, 2014.

Y. Chen, S. Trevezas, and P. Cournède, A Regularized Particle Filter EM Algorithm Based on Gaussian Randomization with an Application to Plant Growth Modeling, Methodology and Computing in Applied Probability, vol.30, issue.1, 2013.
DOI : 10.1007/s11009-015-9440-0

URL : https://hal.archives-ouvertes.fr/hal-00997732

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, pp.112-130, 2013.
DOI : 10.1051/mmnp/20138407

Y. Chen, S. Trevezas, A. Gupta, and P. Cournède, Some sequential Monte Carlo techniques for Data Assimilation in a plant growth model. 15th Conference of Applied Stochastic Models and Data Analysis, Proceedings of ASMDA, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00997736

Y. Chen, S. Trevezas, and P. Cournède, Iterative convolution particle filtering for nonlinear parameter estimation and data assimilation with application to crop yield prediction, SIAM Conference on Control and its Applications (CT13, pp.67-74, 2013.
DOI : 10.1137/1.9781611973273.10

URL : https://hal.archives-ouvertes.fr/hal-00826052

B. Bayol, Y. Chen, and P. Cournède, Towards an EDSL to Enhance Good Modeling Practice for Non-linear Stochastic Discrete Dynamical Models -Application to Plant Growth Models, Third International Conference on Simulation and Modeling Methodologies, Technologies and Applications SciTePress, pp.132-138, 2013.

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, pp.85-92, 2012.
DOI : 10.1109/PMA.2012.6524817

URL : https://hal.archives-ouvertes.fr/hal-00776551

Y. Chen, B. Bayol, C. Loi, S. Trevezas, and P. Cournède, Filtrage par noyaux de convolution itératif, Proceedings of JdS'2012, 2012.

S. Adjemian, Prior distributions in Dynare, 2010.

H. Akaike, A new look at the statistical model identification, IEEE Transactions on Automatic Control, issue.6, pp.716-723, 1974.

B. Anderson and J. Moore, Optimal Filtering, IEEE Transactions on Systems, Man, and Cybernetics, vol.12, issue.2, 1979.
DOI : 10.1109/TSMC.1982.4308806

J. L. Anderson, An Ensemble Adjustment Kalman Filter for Data Assimilation, Monthly Weather Review, vol.129, issue.12, pp.2884-2903, 2001.
DOI : 10.1175/1520-0493(2001)129<2884:AEAKFF>2.0.CO;2

C. Andrieu and A. Doucet, Online expectation-maximization type algorithms for parameter estimation in general state space models, 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)., pp.69-72, 2003.
DOI : 10.1109/ICASSP.2003.1201620

C. Andrieu and G. O. Roberts, The pseudo-marginal approach for efficient Monte Carlo computations, The Annals of Statistics, vol.37, issue.2, pp.697-725, 2009.
DOI : 10.1214/07-AOS574

C. Andrieu and J. Thoms, A tutorial on adaptive MCMC, Statistics and Computing, vol.61, issue.3, pp.343-373, 2008.
DOI : 10.1007/s11222-008-9110-y

C. Andrieu, N. De-freitas, and A. Doucet, Robust Full Bayesian Learning for Radial Basis Networks, Neural Computation, vol.12, issue.261, 2001.
DOI : 10.1162/neco.1997.9.2.461

C. Andrieu, A. Doucet, and R. Holenstein, Particle Markov chain Monte Carlo methods, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.50, issue.3, pp.269-342, 2010.
DOI : 10.1111/j.1467-9868.2009.00736.x

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=

M. Arulampalam, S. Maskell, N. Gordon, and T. 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

K. B. Athreya, H. Doss, and J. Sethuraman, On the convergence of the Markov chain simulation method. The Annals of Statistics, pp.1-448, 1996.

C. Baey, Modélisation de la variabilité inter-individuelle dans les modèles de croissance de plantes et sélection de modèles pour la prévision, 2014.

C. Baey and P. Cournède, Using a hierarchical segmented model to assess the dynamics of leaf appearance in plant populations, Proceedings of ASMDA 2011, 2011.
URL : https://hal.archives-ouvertes.fr/inria-00602419

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

C. Baey, A. Didier, S. Lemaire, F. Maupas, and P. Cournède, Parametrization of five classical plant growth models applied to sugar beet and comparison of their predictive capacity on root yield and total biomass, Ecological Modelling, vol.290, 2013.
DOI : 10.1016/j.ecolmodel.2013.11.003

C. Baey, A. Didier, S. Lemaire, F. Maupas, and P. Cournède, Modelling the interindividual variability of organogenesis in sugar beet populations using a hierarchical segmented model, Ecological Modelling, vol.263, pp.56-63, 2013.
DOI : 10.1016/j.ecolmodel.2013.04.013

URL : https://hal.archives-ouvertes.fr/hal-00819919

C. Baey, S. Trevezas, and P. Cournède, A nonlinear mixed effects model of plant growth and estimation via stochastic variants of the EM algorithm, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01068327

F. Baret, V. Houles, and M. Guérif, Quantification of plant stress using remote sensing observations and crop models: the case of nitrogen management, Journal of Experimental Botany, vol.58, issue.4, pp.869-880, 2007.
DOI : 10.1093/jxb/erl231

T. Bayes, AN ESSAY TOWARDS SOLVING A PROBLEM IN THE DOCTRINE OF CHANCES, Biometrika, vol.45, issue.3-4, p.1763
DOI : 10.1093/biomet/45.3-4.296

B. Bayol, Y. Chen, and P. Cournède, Towards an EDSL to enhance good modeling practice for non-linear stochastic discrete dynamical models -application to plant growth models, International Conference on Simulation and Modeling Methodologies , Technologies and Applications (SIMULTECH), 2013.

M. A. Beaumont, Estimation of population growth or decline in genetically monitored populations, Genetics, vol.164, pp.1139-1160, 2003.

M. Bédard, Optimal acceptance rates for Metropolis algorithms: Moving beyond 0.234, Stochastic Processes and their Applications, pp.2198-2222, 2008.
DOI : 10.1016/j.spa.2007.12.005

J. Berger and R. Wolpert, The Likelihood Principle, 1984.

J. Bertheloot, P. Cournède, and B. Andrieu, NEMA, a functional-structural model of nitrogen economy within wheat culms after flowering. I. Model description, Annals of Botany, vol.108, issue.6, 2011.
DOI : 10.1093/aob/mcr119

URL : https://hal.archives-ouvertes.fr/hal-01019789

J. G. Booth, J. P. Hobert, and W. Jank, A survey of Monte Carlo algorithms for maximizing the likelihood of a two-stage hierarchical model, Statistical Modelling, vol.1, issue.4, pp.333-349, 2001.
DOI : 10.1191/147108201128249

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.265-285, 1999.
DOI : 10.1111/1467-9868.00176

L. Bordes, D. Chauveau, and P. Vandekerkhove, A stochastic EM algorithm for a semiparametric mixture model, Computational Statistics & Data Analysis, vol.51, issue.11, pp.5429-5443, 2007.
DOI : 10.1016/j.csda.2006.08.015

URL : https://hal.archives-ouvertes.fr/hal-00018493

B. Bouman, Linking physical remote sensing models with crop growth simulation models, applied for sugar beet, International Journal of Remote Sensing, vol.267, issue.14, pp.2565-2581, 1992.
DOI : 10.1109/TGRS.1986.289688

E. Bradley and R. Tibshirani, An Introduction to the Bootstrap, 1994.

N. Brisson, B. Mary, M. H. Ripoche, D. Jeuffroy, F. Ruget et al., STICS: a generic model for the simulation of crops and their water and nitrogen balances. I. Theory and parameterization applied to wheat and corn, Agronomie, vol.18, issue.5-6, pp.311-346, 1998.
DOI : 10.1051/agro:19980501

URL : https://hal.archives-ouvertes.fr/hal-00885888

N. Brisson, C. Gary, E. Justes, R. Roche, B. Mary et al., An overview of the crop model stics, European Journal of Agronomy, vol.18, issue.3-4, pp.309-332, 2003.
DOI : 10.1016/S1161-0301(02)00110-7

URL : https://hal.archives-ouvertes.fr/hal-01190851

S. Brooks and G. Roberts, Assessing convergence of Markov chain Monte Carlo algorithms, Statistics and Computing, vol.8, issue.4, pp.319-335, 1998.
DOI : 10.1023/A:1008820505350

G. Buck-sorlin and K. Bachmann, Simulating the morphology of barley spike phenotypes using genotype information, Agronomie, vol.20, issue.6, pp.691-702, 2000.
DOI : 10.1051/agro:2000161

URL : https://hal.archives-ouvertes.fr/hal-00886072

K. Burnham and D. Anderson, Model selection and multimodel inference: a practical information-theoretic approach, 2002.
DOI : 10.1007/b97636

B. S. Caffo, W. Jank, and G. L. Jones, Ascent-based Monte Carlo expectation- maximization, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.11, issue.2, pp.235-251, 2005.
DOI : 10.1111/1467-9868.00334

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. Campillo, R. Rakotozafy, and V. Rossi, Parallel and interacting Markov chain Monte Carlo algorithm, Mathematics and Computers in Simulation, vol.79, issue.12, pp.3424-3433, 2009.
DOI : 10.1016/j.matcom.2009.04.010

URL : https://hal.archives-ouvertes.fr/inria-00506127

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.

O. Cappé, A. Guillin, J. Marin, and C. P. Robert, Population Monte Carlo, Journal of Computational and Graphical Statistics, vol.13, issue.4, pp.907-929, 2004.
DOI : 10.1198/106186004X12803

O. Cappé, E. Moulines, and T. Rydén, Inference in Hidden Markov Models, 2005.

F. Caron, Inférence Bayésienne pour la Détermination et la Sélection de Modèles Stochastiques, 2006.

C. Carter and R. Kohn, On Gibbs sampling for state space models, Biometrika, vol.81, issue.3, pp.541-553, 1994.
DOI : 10.1093/biomet/81.3.541

G. Casella and E. Lehmann, Theory of point estimation, 1998.

J. E. Cavanaugh, Unifying the derivations for the Akaike and corrected Akaike information criteria, Statistics & Probability Letters, vol.33, issue.2, 1997.
DOI : 10.1016/S0167-7152(96)00128-9

G. Celeux and J. Diebolt, The SEM algorithm: a probabilistic teacher algorithm derived from the EM algorithm for the mixture problem, Computational Statistics Quarterly, vol.2, pp.73-82, 1985.

D. Chauveau and J. Diebolt, An automated stopping rule for MCMC convergence assessment, Computational Statistics, vol.14, issue.3, pp.419-442, 1999.
DOI : 10.1007/s001800050024

URL : https://hal.archives-ouvertes.fr/inria-00073116

D. Chauveau and J. Diebolt, Estimation of the Asymptotic Variance in the CLT for Markov Chains, Stochastic Models, vol.2, issue.4, pp.449-465, 2003.
DOI : 10.1080/03610928108828182

URL : https://hal.archives-ouvertes.fr/hal-00202574

D. Chauveau and P. Vandekerkhove, Improving Convergence of the Hastings-Metropolis Algorithm with an Adaptive Proposal, Scandinavian Journal of Statistics, vol.83, issue.1, pp.13-29, 2002.
DOI : 10.1214/aos/1033066201

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

Y. Chen and P. Cournède, Data assimilation to reduce uncertainty of crop yield prediction based on the log-normal allocation and senescence crop model and convolution particle filtering, Ecological Modelling, 2014.

Y. Chen, S. Trevezas, and P. Cournède, Iterative convolution particle filtering for nonlinear parameter estimation and data assimilation with application to crop yield prediction, Society for Industrial and Applied Mathematics (SIAM): Control & its Applications, 2013.
DOI : 10.1137/1.9781611973273.10

URL : https://hal.archives-ouvertes.fr/hal-00826052

Y. Chen, S. Trevezas, and P. Cournède, A Regularized Particle Filter EM Algorithm Based on Gaussian Randomization with an Application to Plant Growth Modeling, Methodology and Computing in Applied Probability, vol.30, issue.1, 2013.
DOI : 10.1007/s11009-015-9440-0

URL : https://hal.archives-ouvertes.fr/hal-00997732

G. Claeskens and N. L. Hjort, Model Selection And Model Averaging. Cambridge Series In Statistical And Probabilistic Mathematics, 2008.

P. Cournède, V. Letort, A. Mathieu, M. Kang, S. Lemaire et al., Some Parameter Estimation Issues in Functional-Structural Plant Modelling, Mathematical Modelling of Natural Phenomena, vol.6, issue.2, pp.133-159, 2011.
DOI : 10.1051/mmnp/20116205

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, pp.112-130, 2013.
DOI : 10.1051/mmnp/20138407

D. Crisan, P. , and T. Lyons, Discrete Filtering Using Branching and Interacting Particle Systems, Markov Processes and Related Fields, pp.293-318, 1998.

F. Buc and N. Brunel, Estimation of Parametric Nonlinear ODEs for Biological Networks Identification, pp.61-96, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00647275

A. Davison and D. Hinkley, Bootstrap Methods and their Application, 1997.
DOI : 10.1017/CBO9780511802843

P. De-reffye, F. Blaise, S. Chemouny, T. Fourcaud, and F. Houllier, Calibration of a hydraulic architecture-based growth model of cotton plants, Agronomie, vol.19, issue.3-4, pp.265-280, 1999.
DOI : 10.1051/agro:19990307

URL : https://hal.archives-ouvertes.fr/hal-00885929

P. De-reffye, M. Goursat, J. Quadrat, and B. Hu, The Dynamic Equations of the Tree Morphogenesis Greenlab Model, 2003.
URL : https://hal.archives-ouvertes.fr/inria-00071706

P. De-valpine, Review of methods for fitting time-series models with process and observation error and likelihood calculations for nonlinear, non-Gaussian state-space models, Bulletin of Marine Science, vol.70, issue.2, pp.455-471, 2002.

P. De-valpine and A. Hastings, FITTING POPULATION MODELS INCORPORATING PROCESS NOISE AND OBSERVATION ERROR, Ecological Monographs, vol.72, issue.1, pp.57-76, 2002.
DOI : 10.1890/0012-9658(1998)079[2193:CPDITR]2.0.CO;2

P. Debashis, P. Jie, and B. Prabir, Semiparametric modeling of autonomous nonlinear dynamical systems with application to plant growth, The Annals of Applied Statistics, vol.5, issue.3, pp.2078-2108, 2011.

P. and D. Moral, Nonlinear filtering: Interacting particle resolution, Markov Processes and Related Fields, pp.555-580, 1996.
DOI : 10.1016/S0764-4442(97)84778-7

R. Delécolle, S. Maas, M. Guérif, and F. Baret, Remote sensing and crop production models: present trends, ISPRS Journal of Photogrammetry and Remote Sensing, vol.47, issue.2-3, pp.47145-161, 1992.
DOI : 10.1016/0924-2716(92)90030-D

C. Deleuze, Pour une dendrométrie fonctionnelle: essai sur l'intégration de connaissancesécophysiologiquessancesécophysiologiques dans les modèles de production ligneuse, 1996.

B. Delyon, M. Lavielle, and E. Moulines, Convergence of a stochastic approximation version of the EM algorithm, Annals of Statistics, vol.27, pp.94-128, 1999.

A. Dempster, The direct use of likelihood for significance testing, Proceedings of Conference on Foundational Questions in Statistical Inference, pp.335-352, 1974.

A. Dempster, N. Laird, and D. Rubin, Maximum Likelihood from incomplete data via the EM algorithm, Journal of the Royal Statistical Society. Series B (Statistical Methodology), vol.39, pp.1-38, 1977.

B. Dennis, J. M. Ponciano, S. R. Lele, M. L. Taper, and D. F. Staples, ESTIMATING DENSITY DEPENDENCE, PROCESS NOISE, AND OBSERVATION ERROR, Ecological Monographs, vol.76, issue.3, pp.323-341, 2006.
DOI : 10.2307/2532752

L. Dente, G. Satalino, F. Mattia, and M. Rinaldi, Assimilation of leaf area index derived from ASAR and MERIS data into CERES-Wheat model to map wheat yield, Remote Sensing of Environment, vol.112, issue.4, pp.1395-1407, 2008.
DOI : 10.1016/j.rse.2007.05.023

L. Devroye and G. Lugosi, Combinatorial Methods in Density Estimation, 2001.
DOI : 10.1007/978-1-4613-0125-7

S. Donnet, J. Foulley, and A. Samson, Bayesian Analysis of Growth Curves Using Mixed Models Defined by Stochastic Differential Equations, Biometrics, vol.10, issue.3, pp.733-741, 2010.
DOI : 10.1111/j.1541-0420.2009.01342.x

URL : https://hal.archives-ouvertes.fr/hal-00360111

W. Dorigo, R. Zurita-milla, A. De-wit, J. Brazile, R. Singh et al., A review on reflective remote sensing and data assimilation techniques for enhanced agroecosystem modeling, International Journal of Applied Earth Observation and Geoinformation, vol.9, issue.2, pp.165-193, 2007.
DOI : 10.1016/j.jag.2006.05.003

R. Douc, O. Cappé, and E. Moulines, Comparison of resampling schemes for particle filtering, ISPA 2005. Proceedings of the 4th International Symposium on Image and Signal Processing and Analysis, 2005., 2005.
DOI : 10.1109/ISPA.2005.195385

URL : https://hal.archives-ouvertes.fr/hal-00005883

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

M. M. Drugan and D. Thierens, Recombinative EMCMC algorithms, 2005 IEEE Congress on Evolutionary Computation, pp.2024-2031, 2005.
DOI : 10.1109/CEC.2005.1554944

B. Efron and R. Tibshirani, An Introduction to the Bootstrap, Chapman & Hall/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

G. Evensen, Data assimilation: The ensemble Kalman Filter, 2006.
DOI : 10.1007/978-3-642-03711-5

P. Fearnhead, Exact and efficient Bayesian inference for multiple changepoint problems, Statistics and Computing, vol.12, issue.2, pp.203-213, 2006.
DOI : 10.1007/s11222-006-8450-8

P. Fearnhead, MCMC for State-Space Models Handbook of Markov chain Monte Carlo, Boca Raton, 2011.

J. M. Flegal and G. L. Jones, Batch means and spectral variance estimators in Markov chain Monte Carlo. The Annals of Statistics, pp.1034-1070, 2010.

E. D. Ford and M. C. Kennedy, Assessment of uncertainty in functional-structural plant models, Annals of Botany, vol.108, issue.6, pp.1043-1053, 2011.
DOI : 10.1093/aob/mcr110

G. Fort and E. Moulines, Convergence of the monte carlo expectation maximization for curved exponential families. The Annals of Statistics, pp.1220-1259, 2003.

J. Foulley and D. A. Van-dyk, The PX-EM algorithm for fast stable fitting of Henderson's mixed model, Genetics Selection Evolution, vol.32, issue.2, pp.1-21, 2012.
DOI : 10.1186/1297-9686-32-2-143

J. Foulley, F. Jaffrézic, and C. Robert-granié, EM-REML estimation of covariance parameters in Gaussian mixed models for longitudinal data analysis, Genetics Selection Evolution, vol.32, issue.2, pp.129-141, 2000.
DOI : 10.1186/1297-9686-32-2-129

URL : https://hal.archives-ouvertes.fr/hal-00894303

T. Fourcaud, X. Zhang, A. Stokes, H. Lambers, and C. Körner, Plant Growth Modelling and Applications: The Increasing Importance of Plant Architecture in Growth Models, Annals of Botany, vol.101, issue.8, 2008.
DOI : 10.1093/aob/mcn050

URL : https://hal.archives-ouvertes.fr/halsde-00281936

D. B. Fowler, A. E. Limin, and J. T. Ritchie, Low-Temperature Tolerance in Cereals: Model and Genetic Interpretation, Crop Science, vol.39, issue.3, pp.626-633, 2003.
DOI : 10.2135/cropsci1999.0011183X003900020002x

B. Gabrielle, R. Roche, P. Angas, C. Cantero-martinez, L. Cosentino et al., A priori parameterisation of the CERES soil-crop models and tests against several European data sets, Agronomie, vol.22, issue.2, pp.22-2119, 2002.
DOI : 10.1051/agro:2002003

C. Gaucherel, F. Campillo, L. Misson, J. Guiot, and J. Boreux, Parameterization of a process-based tree growth model: Comparison of optimization, mcmc and particle filtering algorithms. Environmental Modelling and Software, pp.10-111280, 2008.
URL : https://hal.archives-ouvertes.fr/inria-00506344

A. E. Gelfand, S. E. Hills, A. Racine-poon, and A. F. Smith, Illustration of Bayesian Inference in Normal Data Models Using Gibbs Sampling, Journal of the American Statistical Association, vol.32, issue.412, pp.972-985, 1990.
DOI : 10.1080/01621459.1986.10478240

A. Gelman, C. Robert, N. Chopin, and J. Rousseau, Bayesian data analysis, 1995.

A. Gelman, G. Roberts, and W. Gilks, Efficient Metropolis jumping rules, Bayesian Statistics, pp.599-608, 1996.

A. Gelman, J. B. Carlin, H. Stern, and D. Rubin, Bayesian data analysis, 2004.

D. Gelman, A. , and R. , Inference from Iterative Simulation Using Multiple Sequences, Statistical Science, vol.7, issue.4, pp.457-511, 1992.
DOI : 10.1214/ss/1177011136

S. Geman and D. Geman, Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.6, pp.721-741, 1984.

C. Geyer, Markov chain Monte Carlo maximum likelihood Computing science and Statistics: proceedings of the 23rd Symposium on the interface, pp.156-163, 1991.

C. Geyer, Practical Markov Chain Monte Carlo, Statistical Science, vol.7, issue.4, pp.473-482, 1992.
DOI : 10.1214/ss/1177011137

URL : http://projecteuclid.org/download/pdf_1/euclid.ss/1177011137

W. Gilks, G. Roberts, and E. George, Adaptive Direction Sampling, The Statistician, vol.43, issue.1, pp.179-189, 1994.
DOI : 10.2307/2348942

W. Gilks, G. Roberts, and S. Sahu, Adaptive Markov Chain Monte Carlo through Regeneration, Journal of the American Statistical Association, vol.56, issue.443, pp.1045-1054, 1998.
DOI : 10.1214/aos/1176325750

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=

N. Gordon, D. Salmond, and A. Smith, Novel approach to nonlinear/non-Gaussian Bayesian state estimation, IEE Proceedings F Radar and Signal Processing, vol.140, issue.2, pp.107-113, 1993.
DOI : 10.1049/ip-f-2.1993.0015

M. Guérif and C. Duke, Calibration of the SUCROS emergence and early growth module for sugar beet using optical remote sensing data assimilation, European Journal of Agronomy, vol.9, issue.2-3, pp.127-136, 1998.
DOI : 10.1016/S1161-0301(98)00031-8

M. Guérif and C. Duke, Adjustment procedures of a crop model to the site specific characteristics of soil and crop using remote sensing data assimilation. Agriculture, ecosystems & environment, pp.57-69, 2000.

M. Guérif, V. Houì-es, D. Makowski, and C. Lauvernet, Data assimilation and parameter estimation for precision agriculture using the crop model stics, Working with Dynamic Crop Models, pp.391-398, 2006.

Y. Guo, Y. Ma, Z. Zhan, B. Li, M. Dingkuhn et al., Parameter Optimization and Field Validation of the Functional-Structural Model GREENLAB for Maize, Annals of Botany, vol.97, issue.2, pp.217-230, 2006.
DOI : 10.1093/aob/mcj033

URL : https://hal.archives-ouvertes.fr/inria-00121234

H. Haario, S. J. , and J. Tamminen, Adaptive proposal distribution for random walk Metropolis algorithm, Computational Statistics, vol.14, issue.3, pp.375-395, 1999.
DOI : 10.1007/s001800050022

H. Haario, S. J. , and J. Tamminen, An Adaptive Metropolis Algorithm, Bernoulli, vol.7, issue.2, pp.223-242, 2001.
DOI : 10.2307/3318737

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=

H. Haario, M. Laine, A. Mira, and E. Saksman, DRAM: Efficient adaptive MCMC, Statistics and Computing, vol.18, issue.2, pp.339-354, 2006.
DOI : 10.1007/s11222-006-9438-0

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=

J. Hamilton, Time Series Analysis, 1994.

G. Hammer, M. Cooper, F. Tardieu, S. Welch, B. Walsh et al., Models for navigating biological complexity in breeding improved crop plants, Trends in Plant Science, vol.11, issue.12, pp.11587-593, 2006.
DOI : 10.1016/j.tplants.2006.10.006

J. Harrison and M. West, Bayesian Forecasting and Dynamic Models, 1989.

J. Hartigan, Note on the confidence prior of Welch and Peers, J. Roy. Statist. Soc. B, vol.28, pp.55-56, 1966.

W. Hastings, Monte Carlo sampling methods using Markov chains and their applications, Biometrika, vol.57, issue.1, pp.97-109, 1970.
DOI : 10.1093/biomet/57.1.97

C. Heinrich, The mode functional is not elicitable, Biometrika, vol.101, issue.1, pp.245-251, 2014.
DOI : 10.1093/biomet/ast048

R. Hilborn and M. Ledbetter, Analysis of the British Columbia Salmon Purse-Seine Fleet: Dynamics of Movement, Journal of the Fisheries Research Board of Canada, vol.36, issue.4, pp.384-391, 1979.
DOI : 10.1139/f79-058

J. Hillier, D. Makowski, and B. Andrieu, Maximum Likelihood Inference and Bootstrap Methods for Plant Organ Growth via Multi-phase Kinetic Models and their Application to Maize, Annals of Botany, vol.96, issue.1, pp.137-148, 2005.
DOI : 10.1093/aob/mci159

M. Y. Hirai, M. Yano, D. B. Goodenowe, S. Kanaya, T. Kimura et al., From The Cover: Integration of transcriptomics and metabolomics for understanding of global responses to nutritional stresses in Arabidopsis thaliana, Proceedings of the National Academy of Sciences, vol.101, issue.27, pp.10205-10210, 2004.
DOI : 10.1073/pnas.0403218101

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, vol.24, pp.339-349, 2004.

P. J. Huber, The behavior of maximum likelihood estimation under nonstandard conditions, Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, 1967.

C. M. Hurvich and C. Tsai, Regression and time series model selection in small samples, Biometrika, vol.76, issue.2, pp.297-307, 1989.
DOI : 10.1093/biomet/76.2.297

I. A. Ibragimov and Y. V. Linnik, Independent and Stationary Sequences of Random Variables, Wolters-Noordhoff, 1971.

A. Jasra, D. A. Stephens, and C. C. Holmes, On population-based simulation for static inference, Statistics and Computing, vol.104, issue.6, pp.263-279, 2007.
DOI : 10.1007/s11222-007-9028-9

E. T. Jaynes, Confidence intervals vs Bayesian intervals. Foundations of Probability Theory, Statistical Inference, and Statistical Theories of Science, pp.175-258, 1976.
DOI : 10.1007/978-94-010-1436-6_6

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

C. Jones and J. Kiniry, CERES-Maize : A simulation model of maize growth and development, 1986.

G. Jones and J. Hobert, Honest Exploration of Intractable Probability Distributions via Markov Chain Monte Carlo, Statistical Science, vol.16, issue.4, pp.312-334, 2001.
DOI : 10.1214/ss/1015346317

G. Jones and J. Hobert, Sufficient burn-in for Gibbs samplers for a hierarchical random effects model. The Annals of Statistics, pp.784-817, 2004.

G. L. Jones, M. Haran, B. S. Caffo, and R. Neath, Fixed-Width Output Analysis for Markov Chain Monte Carlo, Journal of the American Statistical Association, vol.101, issue.476, pp.1537-1547, 2006.
DOI : 10.1198/016214506000000492

J. Jones and W. Graham, Application of extended and ensemble Kalman filters to soil carbon estimation, Working with Dynamic Crop Models, pp.55-100, 2006.

S. Julier and J. Uhlmann, New extension of the Kalman filter to nonlinear systems, Signal Processing, Sensor Fusion, and Target Recognition VI, 1997.
DOI : 10.1117/12.280797

S. Julier, J. Uhlmann, and H. Durrant-whyte, A new method for the nonlinear transformation of means and covariances in filters and estimators, IEEE Transactions on Automatic Control, vol.45, issue.3, pp.477-482, 2000.
DOI : 10.1109/9.847726

A. Jullien, J. Allirand, A. Mathieu, B. Andrieu, and B. Ney, Variations in leaf mass per area according to nitrogen nutrition, plant age, and leaf position reflect ontogenetic plasticity in winter oilseed rape (brassica napus l.). Field Crops Research, pp.188-197, 2009.

J. Kaipio and E. Somersalo, Statistical and Computational Inverse problems, Applied Mathematical Science, 2005.

R. Kalman, A New Approach to Linear Filtering and Prediction Problems, Journal of Basic Engineering, vol.82, issue.1, pp.35-45, 1960.
DOI : 10.1115/1.3662552

B. Keating, P. Carberry, G. Hammer, M. Probert, M. Robertson et al., An overview of APSIM, a model designed for farming systems simulation, European Journal of Agronomy, vol.18, issue.3-4, pp.3-4267, 2003.
DOI : 10.1016/S1161-0301(02)00108-9

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

J. R. Koehler and A. Owen, 9 Computer experiments, Handbook of statistics 13, 1996.
DOI : 10.1016/S0169-7161(96)13011-X

A. Kong, J. Liu, and W. Wong, Sequential Imputations and Bayesian Missing Data Problems, Journal of the American Statistical Association, vol.52, issue.425, pp.278-288, 1994.
DOI : 10.1080/01621459.1987.10478458

M. Lamboni, H. Monod, and D. Makowski, Multivariate sensitivity analysis to measure global contribution of input factors in dynamic models, Reliability Engineering & System Safety, vol.96, issue.4, pp.450-459, 2011.
DOI : 10.1016/j.ress.2010.12.002

URL : https://hal.archives-ouvertes.fr/hal-00999840

K. B. Laskey and J. Myers, Population Markov chain Monte Carlo, Machine Learning, pp.175-196, 2003.

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

F. , L. Gland, and N. Oudjane, Stability and uniform approximation of nonlinear filters using the Hilbert metric and application to particle filters, The Annals of Applied Probability, vol.14, issue.1, pp.144-187, 2004.
DOI : 10.1214/aoap/1075828050

URL : https://hal.archives-ouvertes.fr/hal-00912081

F. Le-gland, C. Musso, and N. Oudjane, An Analysis of Regularized Interacting Particle Methods for Nonlinear Filtering, Proceedings of the 3rd IEEE European Workshop on Computer-Intensive Methods in Control and Signal Processing, pp.167-174, 1998.

J. Lecoeur, R. Poiré-lassus, A. Christophe, B. Pallas, P. Casadebaig et al., Quantifying physiological determinants of genetic variation for yield potential in sunflower. SUNFLO: a model-based analysis, Functional Plant Biology, vol.38, issue.3, pp.246-259, 2011.
DOI : 10.1071/FP09189

URL : https://hal.archives-ouvertes.fr/hal-00964303

S. Lemaire, F. Maupas, P. Cournède, and P. De-reffye, A Morphogenetic Crop Model for Sugar-Beet (Beta vulgaris L.), International Symposium on Crop Modeling and Decision Support: ISCMDS 2008, 2008.
DOI : 10.1007/978-3-642-01132-0_14

URL : https://hal.archives-ouvertes.fr/inria-00336415

S. Lemaire, F. Maupas, P. Cournède, J. Allirand, P. De-reffye et al., Analysis of the Density Effects on the Source-sink Dynamics in Sugar-Beet Growth, 2009 Third International Symposium on Plant Growth Modeling, Simulation, Visualization and Applications
DOI : 10.1109/PMA.2009.77

URL : https://hal.archives-ouvertes.fr/inria-00543137

R. A. Levine and G. Casella, Implementations of the Monte Carlo EM Algorithm, Journal of Computational and Graphical Statistics, vol.10, issue.3, pp.422-439, 2001.
DOI : 10.1198/106186001317115045

J. Liu, Fraction of missing information and convergence rate of data augmentation. Interface Foundation of North America, 1994.

J. Liu, W. Wong, and A. Kong, Covariance structure of the Gibbs sampler with applications to the comparisons of estimators and augmentation schemes, Biometrika, vol.81, issue.1, pp.27-40, 1994.
DOI : 10.1093/biomet/81.1.27

C. Loi, Analyse probabiliste, ´ etude combinatoire et estimation paramétrique pour une classe de modèles de croissance de plantes avec organogenèse stochastique, 2011.

C. Loi, P. Cournède, and S. Trevezas, Bayesian estimation in Functional-Structural Plant Models with stochastic organogenesis, Proceedings of ASMDA, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00653672

D. Ludwig and C. Walters, Measurement Errors and Uncertainty in Parameter Estimates for Stock and Recruitment, Canadian Journal of Fisheries and Aquatic Sciences, vol.38, issue.6, pp.711-720, 1981.
DOI : 10.1139/f81-094

S. J. Maas, Using Satellite Data to Improve Model Estimates of Crop Yield, Agronomy Journal, vol.80, issue.4, pp.655-662, 1988.
DOI : 10.2134/agronj1988.00021962008000040021x

J. Mailhol, A. Olufayo, and P. Ruelle, Sorghum and sunflower evapotranspiration and yield from simulated leaf area index, Agricultural Water Management, vol.35, issue.1-2, pp.167-182, 1997.
DOI : 10.1016/S0378-3774(97)00029-2

D. Makowski, D. Wallach, and M. Tremblay, Using a Bayesian approach to parameter estimation; comparison of the GLUE and MCMC methods, Agronomie, vol.22, issue.2, pp.191-203, 2002.
DOI : 10.1051/agro:2002007

D. Makowski, J. Jeuffroy, and M. Guérif, Bayesian methods for updating crop-model predictions, applications for predicting biomass and grain protein content, 2004.

D. Makowski, J. Hillier, D. Wallach, B. Andrieu, and M. Jeuffroy, Parameter estimation for crop models, Working with Dynamic Crop Models, pp.55-100, 2006.

M. Matsumoto and T. Nishimura, Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator, ACM Transactions on Modeling and Computer Simulation, vol.8, issue.1, pp.3-30, 1998.
DOI : 10.1145/272991.272995

J. P. Mattern, M. Dowd, and K. Fennel, Particle filter-based data assimilation for a three-dimensional biological ocean model and satellite observations, Journal of Geophysical Research: Oceans, vol.230, issue.12, pp.2746-2760, 2013.
DOI : 10.1016/j.physd.2006.09.017

C. E. Mcculloch, Maximum Likelihood Variance Components Estimation for Binary Data, Journal of the American Statistical Association, vol.36, issue.425, pp.330-335, 1994.
DOI : 10.2307/2531734

C. E. Mcculloch, Maximum Likelihood Algorithms for Generalized Linear Mixed Models, Journal of the American Statistical Association, vol.86, issue.437, pp.162-170, 1997.
DOI : 10.1080/01621459.1997.10473613

G. Mclachlan and T. Krishnan, The EM Algorithm and Extensions, 2008.

X. Meng and D. B. Rubin, Maximum likelihood estimation via the ECM algorithm: A general framework, Biometrika, vol.80, issue.2, pp.267-278, 1993.
DOI : 10.1093/biomet/80.2.267

K. L. Mengersen and C. P. Robert, Population Markov chain Monte Carlo: the pinball sampler, Bayesian Statistics, 2003.

N. Metropolis, A. Rosenbluth, M. Rosenbluth, A. Teller, and E. Teller, Equation of State Calculations by Fast Computing Machines, The Journal of Chemical Physics, vol.21, issue.6, pp.1087-1091, 1953.
DOI : 10.1063/1.1699114

C. Meza, F. Jaffrézic, and J. Foulley, REML Estimation of Variance Parameters in Nonlinear Mixed Effects Models Using the SAEM Algorithm, Biometrical Journal, vol.85, issue.6, pp.876-88, 2007.
DOI : 10.1002/bimj.200610348

R. Mittler, Abiotic stress, the field environment and stress combination, Trends in Plant Science, vol.11, issue.1, pp.15-19, 2006.
DOI : 10.1016/j.tplants.2005.11.002

H. Monod, C. Naud, and D. Makowski, Uncertainty and sensitivity analysis for crop models, Working with Dynamic Crop Models, pp.55-100, 2006.

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

M. Moran, Y. Inoue, and E. Barnes, Opportunities and limitations for image-based remote sensing in precision crop management, Remote Sensing of Environment, vol.61, issue.3, pp.319-346, 1997.
DOI : 10.1016/S0034-4257(97)00045-X

S. Moulin, A. Bondeau, and R. Delecolle, Combining agricultural crop models and satellite observations: From field to regional scales, International Journal of Remote Sensing, vol.19, issue.6, pp.1021-1036, 1998.
DOI : 10.1080/014311698215586

L. M. Murray, Distributed Markov chain Monte Carlo, NIPS Workshop: Learning on Cores, Clusters and Clouds, 2010.

C. Musso and N. Oudjane, Regularization schemes for branching particle systems as a numerical solving method of the nonlinear filtering problem, Proceedings of the Irish Signals and Systems Conference, 1998.

P. Mykland, L. Tierney, and B. Yu, Regeneration in Markov Chain Samplers, Journal of the American Statistical Association, vol.21, issue.429, pp.233-241, 1995.
DOI : 10.1080/01621459.1995.10476507

C. Naud, D. Makowski, and M. Jeuffroy, Application of an interacting particle filter to improve nitrogen nutrition index predictions for winter wheat, Ecological Modelling, vol.207, issue.2-4, pp.251-263, 2007.
DOI : 10.1016/j.ecolmodel.2007.05.003

URL : https://hal.archives-ouvertes.fr/hal-01173125

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

N. Oudjane and C. Musso, Multiple model particle filter, 17ème Colloque GRETSI, pp.681-684, 1999.

E. Parzen, On Estimation of a Probability Density Function and Mode, The Annals of Mathematical Statistics, vol.33, issue.3, pp.1065-1076, 1962.
DOI : 10.1214/aoms/1177704472

D. Pham, Stochastic Methods for Sequential Data Assimilation in Strongly Nonlinear Systems, Monthly Weather Review, vol.129, issue.5, pp.217-244, 2001.
DOI : 10.1175/1520-0493(2001)129<1194:SMFSDA>2.0.CO;2

URL : https://hal.archives-ouvertes.fr/inria-00073082

T. Polacheck, R. Hilborn, and A. Punt, Fitting Surplus Production Models: Comparing Methods and Measuring Uncertainty, Canadian Journal of Fisheries and Aquatic Sciences, vol.50, issue.12, pp.2597-2607, 1993.
DOI : 10.1139/f93-284

B. T. Polyak and A. B. Juditsky, Acceleration of Stochastic Approximation by Averaging, SIAM Journal on Control and Optimization, vol.30, issue.4, pp.838-855, 1992.
DOI : 10.1137/0330046

M. Quach, N. Brunel, and F. Buc, Estimating parameters and hidden variables in non-linear state-space models based on ODEs for biological networks inference, Bioinformatics, vol.23, issue.23, pp.3209-3216, 2007.
DOI : 10.1093/bioinformatics/btm510

URL : https://hal.archives-ouvertes.fr/hal-00341950

L. Rabiner, A tutorial on hidden Markov models and selected applications in speech recognition, Proc. IEEE, pp.257-284, 1989.

L. R. Rabiner and B. H. Juang, An introduction to hidden Markov models, IEEE ASSP Magazine, vol.3, issue.1, pp.4-15, 1986.
DOI : 10.1109/MASSP.1986.1165342

C. Rao, Linear Statistical Inference and Its Applications, 1973.
DOI : 10.1002/9780470316436

A. Rau, F. Jaffrézic, J. Foulley, and R. Doerge, An Empirical Bayesian Method for Estimating Biological Networks from Temporal Microarray Data, Statistical Applications in Genetics and Molecular Biology, vol.9, issue.1, pp.1-28, 2010.
DOI : 10.2202/1544-6115.1513

URL : https://hal.archives-ouvertes.fr/hal-01193424

A. Rau, F. Jaffrézic, J. Foulley, and R. Doerge, Reverse engineering gene regulatory networks using approximate Bayesian computation, Statistics and Computing, vol.10, issue.1, pp.1257-1271, 2011.
DOI : 10.1007/s11222-011-9309-1

URL : https://hal.archives-ouvertes.fr/hal-01019032

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

B. Ripley, Stochastic Simulation, 1988.
DOI : 10.1002/9780470316726

H. Robbins and S. Monro, A stochastic approximation method. The Annals of Mathematical Statistics, pp.400-407, 1951.

C. Robert, The Bayesian Choice. Springer Texts in Statistics Series, 2001.

C. Robert and G. Casella, Monte Carlo Statistical Methods. Springer Texts in Statistics Series, 1999.

C. Robert, T. Rydén, and D. Titterington, Convergence controls for MCMC algorithms, with applications to hidden markov chains, Journal of Statistical Computation and Simulation, vol.26, issue.4, pp.327-355, 1999.
DOI : 10.1023/A:1008938201645

C. P. Robert and G. Casella, Introducing Monte Carlo methods with R, 2010.
DOI : 10.1007/978-1-4419-1576-4

URL : https://hal.archives-ouvertes.fr/hal-00450502

G. Roberts, Markov chain concepts related to sampling algorithms. Markov Chain Monte Carlo in Practice, pp.45-57, 1996.

G. Roberts and J. Rosenthal, General state space Markov chains and MCMC algorithms, Probability Surveys, vol.1, issue.0, pp.20-71, 2004.
DOI : 10.1214/154957804100000024

G. Roberts and S. K. Sahu, Updating Schemes, Correlation Structure, Blocking and Parameterization for the Gibbs Sampler, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.59, issue.2, pp.291-317, 1997.
DOI : 10.1111/1467-9868.00070

G. Roberts, A. Gelman, and W. Gilks, The annals of applied probability

J. S. Rosenthal, Minorization Conditions and Convergence Rates for Markov Chain Monte Carlo, Journal of the American Statistical Association, vol.3, issue.430, pp.558-566, 1995.
DOI : 10.1080/01621459.1987.10478458

V. Rossi, Filtrage non linéaire par noyaux de convolution: ApplicationàApplicationà un procédé de dépollution biologique, 2004.

V. Rossi and J. Vila, Nonlinear filtering in discrete time: A particle convolution approach, Ann. Inst. Stat. Univ. Paris, vol.3, pp.71-102, 2006.

D. Rubin, Bayesianly Justifiable and Relevant Frequency Calculations for the Applied Statistician, The Annals of Statistics, vol.12, issue.4, pp.1151-1172, 1984.
DOI : 10.1214/aos/1176346785

S. Sahu and A. A. Zhigljavsky, Self-regenerative Markov chain Monte Carlo with adaptation, Bernoulli, vol.9, issue.3, pp.395-422, 2003.
DOI : 10.3150/bj/1065444811

A. Saltelli, S. Tarantola, F. Campolongo, and M. Ratto, Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models, 2004.
DOI : 10.1002/0470870958

R. L. Schnute and J. T. , The influence of error on population estimates from catch-age models, Canadian Journal of Fisheries and Aquatic Sciences, vol.52, issue.10, pp.2063-2077, 1995.
DOI : 10.1139/f95-800

G. Schwarz, Estimating the Dimension of a Model, The Annals of Statistics, vol.6, issue.2, pp.461-464, 1978.
DOI : 10.1214/aos/1176344136

S. L. Scott, Bayesian Methods for Hidden Markov Models, Journal of the American Statistical Association, vol.97, issue.457, pp.337-351, 2002.
DOI : 10.1198/016214502753479464

S. R. Searle, G. Casella, and C. E. Mcculloch, Variance components, 1992.
DOI : 10.1002/9780470316856

N. Shephard and M. K. Pitt, Likelihood analysis of non-Gaussian measurement time series, Biometrika, vol.84, issue.3, pp.653-667, 1997.
DOI : 10.1093/biomet/84.3.653

B. Silverman, Density Estimation, 1986.

A. Smith and G. Roberts, Bayesian computation via the Gibbs sampler and related Markov chain Monte Carlo methods, J. Roy. Statist. Soc. Ser., B, issue.55, pp.3-24, 1993.

I. Sobol, Sensitivity analysis for non-linear mathematical models, Mathematical Modeling and Computational Experiment, vol.1, pp.407-414, 1993.

H. Sorenson, Kalman Filtering: Theory and Applications, 1985.

D. J. Spiegelhalter, N. G. Best, and B. P. Carlin, Bayesian deviance, the effective number of parameters, and the comparison of arbitrarily complex models, 1998.

C. Spitters, H. Van-keulen, and D. Van-kraalingen, A simple and universal crop growth simulator: SUCROS87. PUDOC, 1989.

G. Storvik, Particle filters for state-space models with the presence of unknown static parameters, IEEE Transactions on Signal Processing, vol.50, issue.2, pp.281-289, 2002.
DOI : 10.1109/78.978383

M. A. Tanner and W. H. Wong, The Calculation of Posterior Distributions by Data Augmentation, Journal of the American Statistical Association, vol.56, issue.398, pp.528-550, 1987.
DOI : 10.1016/0304-4076(84)90007-1

F. Tardieu, Virtual plants: modelling as a tool for the genomics of tolerance to water deficit, Trends in Plant Science, vol.8, issue.1, pp.9-14, 2003.
DOI : 10.1016/S1360-1385(02)00008-0

W. Taylor, Small Sample Properties of a Class of Two Stage Aitken Estimators, Econometrica, vol.45, issue.2, pp.497-508, 1977.
DOI : 10.2307/1911224

C. J. Braak, A Markov Chain Monte Carlo version of the genetic algorithm Differential Evolution: easy Bayesian computing for real parameter spaces, Statistics and Computing, vol.69, issue.3, pp.239-249, 2006.
DOI : 10.1007/s11222-006-8769-1

C. J. Ter-braak and J. A. Vrugt, Differential Evolution Markov Chain with snooker updater and??fewer chains, Statistics and Computing, vol.69, issue.4, pp.435-446, 2008.
DOI : 10.1007/s11222-008-9104-9

S. Trevezas and P. Cournède, A Sequential Monte Carlo Approach for MLE in a Plant Growth Model, Journal of Agricultural, Biological, and Environmental Statistics, vol.12, issue.2, pp.250-270, 2013.
DOI : 10.1007/s13253-013-0134-1

URL : https://hal.archives-ouvertes.fr/hal-00796154

S. Trevezas, S. Malefaki, and P. Cournède, Simulation techniques for parameter estimation via a stochastic ECM algorithm with application to plant growth modeling, 2013.

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

H. Varella, M. Guerif, S. Buis, and N. Beaudoin, Soil properties estimation by inversion of a crop model and observations on crops improves the prediction of agro-environmental variables, European Journal of Agronomy, vol.33, issue.2, pp.139-147, 2010.
DOI : 10.1016/j.eja.2010.04.005

J. A. Vrugt, C. J. Ter-braak, C. G. Diks, B. A. Robinson, J. M. Hyman et al., Accelerating Markov Chain Monte Carlo Simulation by Differential Evolution with Self-Adaptive Randomized Subspace Sampling, International Journal of Nonlinear Sciences and Numerical Simulation, vol.10, issue.3, pp.273-290, 2009.
DOI : 10.1515/IJNSNS.2009.10.3.273

J. A. Vrugt, C. J. Ter-braak, H. V. Gupta, and B. A. Robinson, Equifinality of formal (DREAM) and informal (GLUE) Bayesian approaches in hydrologic modeling? Stochastic Environmental Research and Risk Assessment, pp.1011-1026, 2009.

D. Wallach, B. Goffinet, J. Bergez, P. Debaeke, D. Leenhardt et al., The effect of parameter uncertainty on a model with adjusted parameters, Agronomie, vol.22, issue.2, pp.159-170, 2002.
DOI : 10.1051/agro:2002006

D. Wallach, D. Makowski, and J. Jones, Working with Dynamic Crop Models: Evaluation , Analysis, Parameterization, and Applications, chapter Evaluating crop models, pp.11-53, 2006.

E. Wan and R. Van-der-merwe, The unscented Kalman filter for nonlinear estimation, Proceedings of the IEEE 2000 Adaptive Systems for Signal Processing, Communications, and Control Symposium (Cat. No.00EX373), 2000.
DOI : 10.1109/ASSPCC.2000.882463

Y. Wang, Y. Wang, G. Wahba, and G. Wahba, Bootstrap confidence intervals for smoothing splines and their comparison to bayesian confidence intervals, Journal of Statistical Computation and Simulation, vol.45, issue.2-4, pp.263-279, 1994.
DOI : 10.2307/2287973

G. Wei and M. Tanner, A Monte Carlo Implementation of the EM Algorithm and the Poor Man's Data Augmentation Algorithms, Journal of the American Statistical Association, vol.51, issue.411, pp.699-704, 1990.
DOI : 10.1214/aos/1176346060

H. Wernsdörfer, V. Rossi, G. Cornu, S. Oddou-muratorio, and S. Gourlet-fleury, Impact of uncertainty in tree mortality on the predictions of a tropical forest dynamics model, Ecological Modelling, vol.218, issue.3-4, pp.290-306, 2008.
DOI : 10.1016/j.ecolmodel.2008.07.017

M. West, Approximating posterior distribution by mixtures, Journal of Royal Statistical Society, B, issue.55, pp.409-442, 1993.

Q. Wu, J. Bertheloot, A. Mathieu, B. Andrieu, and P. Cournède, Assessment of non-linearity in functional-structural plant models, 6th international workshop on Functional-Structural Plant Models (FSPM10), 2010.
URL : https://hal.archives-ouvertes.fr/hal-01192293

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

P. Wyckoff and J. Clark, Predicting tree mortality from diameter growth: a comparison of maximum likelihood and Bayesian approaches, Canadian Journal of Forest Research, vol.30, issue.1, pp.156-167, 2000.
DOI : 10.1139/x99-198

H. Yan, M. Kang, P. De-reffye, and M. Dingkuhn, A Dynamic, Architectural Plant Model Simulating Resource-dependent Growth, Annals of Botany, vol.93, issue.5, pp.591-602, 2004.
DOI : 10.1093/aob/mch078

URL : https://hal.archives-ouvertes.fr/inria-00122497

X. Yin and P. C. Struik, Modelling the crop: from system dynamics to systems biology, Journal of Experimental Botany, issue.8, pp.612171-2183, 2010.

P. Yiou, AnaWEGE: a weather generator based on analogues of atmospheric circulation, Geoscientific Model Development, vol.7, issue.2, pp.531-543, 2014.
DOI : 10.5194/gmd-7-531-2014

Z. Zhan, P. De-reffye, F. Houllier, and B. Hu, Fitting a structural-functional model with plant architectural data, Plant Growth Models and Applications, pp.236-249, 2003.
URL : https://hal.archives-ouvertes.fr/inria-00122502