C. Andrieu and ´. E. Moulines, On the ergodicity properties of some adaptive MCMC algorithms, The Annals of Applied Probability, vol.16, issue.3, 2006.
DOI : 10.1214/105051606000000286

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. Baey, A. Didier, S. Lemaire, F. Maupas, C. et al., 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

J. Brouwer, L. K. Fussell, H. , and L. , Soil and crop growth micro-variability in the West African semi-arid tropics: a possible risk-reducing factor for subsistence farmers, Agriculture, Ecosystems & Environment, vol.45, issue.3-4, pp.3-4229, 1993.
DOI : 10.1016/0167-8809(93)90073-X

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. Campolongo, J. Cariboni, and A. Saltelli, An effective screening design for sensitivity analysis of large models, Environmental Modelling & Software, vol.22, issue.10, pp.1509-1518, 2007.
DOI : 10.1016/j.envsoft.2006.10.004

O. Cappé, E. Moulines, and T. And-ryden, Inference in Hidden Markov Models, 2005.

Y. Chen, S. Trevezas, C. , and P. , 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, 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, A. Mathieu, F. Houllier, D. Barthélémy, and P. De-reffye, Computing Competition for Light in the GREENLAB Model of Plant Growth: A Contribution to the Study of the Effects of Density on Resource Acquisition and Architectural Development, Annals of Botany, vol.101, issue.8, pp.1207-1219, 2008.
DOI : 10.1093/aob/mcm272

M. Davidian and D. M. Giltinan, Nonlinear Models for Repeated Measurement Data, 1995.

M. Davidian and D. M. Giltinan, Nonlinear models for repeated measurement data: An overview and update, Journal of Agricultural, Biological, and Environmental Statistics, vol.16, issue.4, pp.387-419, 2003.
DOI : 10.1198/1085711032697

P. De-reffye and B. Hu, Relevant qualitative and quantitative choices for building an efficient dynamic plant growth model: GreenLab case, First International Symposium on Plant Growth Modeling, Simulation, Visualization and Applications (PMA), pp.87-107, 2003.
URL : https://hal.archives-ouvertes.fr/inria-00126213

B. Delyon, M. Lavielle, and E. Moulines, Convergence of a stochastic approximation version of the EM algorithm, The Annals of Statistics, vol.27, issue.1, pp.94-128, 1999.

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

B. Efron and D. V. Hinkley, Assessing the accuracy of the maximum likelihood estimator: Observed versus expected Fisher information, Biometrika, vol.65, issue.3, pp.457-487, 1978.
DOI : 10.1093/biomet/65.3.457

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

C. Fournier and B. Andrieu, ADEL-maize: an L-system based model for the integration of growth processes from the organ to the canopy. Application to regulation of morphogenesis by light availability, Agronomie, vol.19, issue.3-4, pp.3-4313, 1999.
DOI : 10.1051/agro:19990311
URL : https://hal.archives-ouvertes.fr/hal-00885933

H. Haario, E. Saksman, and J. And-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=10.1.1.48.8948

W. Jank, Stochastic Variants of EM: Monte Carlo, Quasi?Monte Carlo and More, Proceedings of the American Statistical Association, 2005.

W. Jank, The EM algorithm, Its Stochastic Implementation and Global Optimization: Some Challenges and Opportunities for OR, Topics in Modeling, Optimization and Decision Technologies: Honoring Saul Gass' Contributions to Operation Research, pp.367-392, 2006.

W. Jank, Implementing and Diagnosing the Stochastic Approximation EM Algorithm, Journal of Computational and Graphical Statistics, vol.15, issue.4, pp.1-30, 2006.
DOI : 10.1198/106186006X157469

E. Kuhn and M. Lavielle, Coupling a stochastic approximation version of EM with an MCMC procedure, ESAIM: Probability and Statistics, vol.8, pp.115-131, 2004.
DOI : 10.1051/ps:2004007

E. Kuhn and M. Lavielle, Maximum likelihood estimation in nonlinear mixed effects models, Computational Statistics & Data Analysis, vol.49, issue.4, pp.1020-1038, 2005.
DOI : 10.1016/j.csda.2004.07.002

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

T. A. Louis, Finding the Observed Information Matrix when using the EM-algorithm, Journal of the Royal Statistical Society, vol.44, issue.2, pp.226-233, 1982.

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, 2007.

T. Orchard and M. Woodbury, A missing information principle: theory and applications, 1972.

A. Racine-poon, A Bayesian Approach to Nonlinear Random Effects Models, Biometrics, vol.41, issue.4, pp.1015-1023, 1985.
DOI : 10.2307/2530972

J. Renno and T. Winkel, under experimental conditions in the Sahel: implications for the maintenance of polymorphism in the species, Canadian Journal of Botany, vol.74, issue.6, pp.959-964, 1996.
DOI : 10.1139/b96-119
URL : https://hal.archives-ouvertes.fr/ird-00142188

H. Robbins and S. Monro, A stochastic approximation method, Ann. Math. Statist, vol.22, p.400407, 1951.

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

G. O. Roberts, A. Gelman, and W. R. Gilks, Weak convergence and optimal scaling of random walk metropolis algorithms. The annals of applied probability, pp.110-120, 1997.

R. Sievänen, E. Nikinmaa, P. Nygren, H. Ozier-lafontaine, J. Perttunen et al., Components of functional-structural tree models, Annals of Forest Science, vol.57, issue.5, pp.399-412, 2000.
DOI : 10.1051/forest:2000131

R. Sundberg, Maximum likelihood theory for incomplete data from an exponential family, Scandinavian Journal of Statistics, vol.1, pp.49-58, 1974.

L. Tierney, Markov chains for exploring posterior distributions. The Annals of Statistics, pp.1701-1728, 1994.

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, C. , and P. , Simulation techniques for parameter estimation via a stochastic ECM algorithm with applications to plant growth modeling, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00798695

J. Vos, L. Marcelis, and J. Evers, Functional-structural plant modelling in crop production, 2007.

G. C. Wei and M. A. 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.85699-704, 1990.
DOI : 10.1214/aos/1176346060

C. F. Wu, On the convergence properties of the EM algorithm. The Annals of Statistics, 1983.