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All issues Volume 6 / No 2 (2011) Math. Model. Nat. Phenom., 6 2 (2011) 133-159 References
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Issue
Math. Model. Nat. Phenom.
Volume 6, Number 2, 2011
Modelling of plant growth
Page(s) 133 - 159
DOI https://doi.org/10.1051/mmnp/20116205
Published online 01 March 2011
  1. M.T. Allen, P. Prusinkiewicz, T.M. Dejong. Using L-systems for modeling source-sink interactions, architecture and physiology of growing trees, the L-peach model. New Phytologist, 166 (2005), 869–880. [CrossRef] [Google Scholar]
  2. E. Assmann. The principles of forest yield study. Perfamon Press Ltd., Oxford, 1970. [Google Scholar]
  3. D. Barthélémy, Y. Caraglio, E. Costes. Architecture, gradients morphogénétiques et âge physiologique chez les végétaux. Modélisation et simulation de l’architecture des végétaux, (J. Bouchon, ed.), Sciences Update, INRA, 1997, 89–136. [Google Scholar]
  4. D. Barthélémy, Y. Caraglio. Plant architecture: a dynamic, multilevel and comprehensive approach to plant form, structure and ontogeny. Annals of Botany, 99 (2007), No. 3, 375–407. [Google Scholar]
  5. N. Brisson, C. Gary, E. Justes, R. Roche, B. Mary, D. Ripoche, D. Zimmer, J. Sierra, P. Bertuzzi, P. Burger, F. Bussière, Y.M. Cabidoche, P. Cellier, P. Debaeke, J.P. Gaudillère, C. Hénault, F. Maraux, B. Seguin, H. Sinoquet. An overview of the crop model STICS. European Journal of Agronomy, 18 (2003), 309–332. [CrossRef] [Google Scholar]
  6. O. Cappé, E. Moulines, T. Rydén. Inference in hidden Markov models, Springer, New York, 2005. [Google Scholar]
  7. A. Christophe, V. Letort, I. Hummel, P.-H. Cournède, P. de Reffye, J. Lecoeur. A model based analysis of the dynamics of carbon balance at the whole plant level in Arabidopsis Thaliana. Functional Plant Biology, 35(11) (2008), 1147–1162. [CrossRef] [Google Scholar]
  8. P.-H. Cournède. Dynamic system of plant growth. HDR Thesis, University of Montpellier II, 2009. [Google Scholar]
  9. P.-H. Cournède, M.Z. Kang, A. Mathieu, J.-F. Barczi, H.P. Yan, B.G. Hu, P. de Reffye, Structural factorization of plants to compute their functional and architectural growth. Simulation, 82 (2006), No. 7, 427–438. [CrossRef] [Google Scholar]
  10. P.-H. Cournède, A. Mathieu, F. Houllier, D. Barthélémy, 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, 101 (2008), No. 8, 1207–1219. [CrossRef] [PubMed] [Google Scholar]
  11. H. Cramer. Mathematical methods of statistics. Princeton University Press, Princeton, 1946. [Google Scholar]
  12. P. de Reffye, F. Blaise, S. Chemouny, T. Fourcaud, F. Houllier. Calibration of hydraulic growth model on the architecture of cotton plants. Agronomie, 19 (1999), 265–280. [CrossRef] [EDP Sciences] [Google Scholar]
  13. P. de Reffye, E. Elguero, E. Costes. Growth units construction in trees: a stochastic approach. Acta Biotheoretica, 39 (1991), 325–342. [CrossRef] [Google Scholar]
  14. P. de Reffye, E. Heuvelink, D. Barthélémy, P.-H. Cournède. Plant growth models. Ecological Models, Vol. 4 of Encyclopedia of Ecology (5 volumes) (S.E. Jorgensen and B. Fath, eds.), Elsevier, Oxford, 2008, 2824–2837. [Google Scholar]
  15. P. de Reffye, S. Lemaire, N. Srivastava, F. Maupas, P.-H. Cournède. Modeling inter-individual variability in sugar beet populations. 3rd international symposium on Plant Growth and Applications(PMA09), Beijing, China (B.G. Li, M. Jaeger, Y. Guo, eds.), IEEE, November 9-12 2009. [Google Scholar]
  16. A.P. Dempster, N.M. Laird, D.B. Rubin. Maximum Likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society. Series B (Statistical Methodology), 39 (1977), 1–38. [Google Scholar]
  17. A. Doucet, A.M. Johansen. A tutorial on particle filtering and smoothing: fifteen years later. Tech. report, Department of Statistics, University of British Columbia, 2008. [Google Scholar]
  18. J.-L. Drouet, L. Pagès. GRAAL: a model of GRowth Architecture and carbon ALlocation during the vegetative phase of the whole maize plant, model description and parameterisation. Ecological Modelling, 165 (2003), 147–173. [CrossRef] [Google Scholar]
  19. C. Eschenbach. Emergent properties modelled with the functional structural tree growth model almis: Computer experiments on resource gain and use. Ecological Modelling, 186 (2005), No. 4, 470 – 488. [CrossRef] [Google Scholar]
  20. J.F. Farrar. Sink strength: What is it and how do we measure it? A summary. Plant, Cell and Environment, 16 (1993), No. 9, 1045–1046. [Google Scholar]
  21. T. Fourcaud, X.P. Zhang, A. Stokes, H. Lambers, C. Körner. Plant growth modelling and applications: The increasing importance of plant architecture in growth models. Annals of Botany, 101 (2008), No. 8, 1053–1063. [CrossRef] [PubMed] [Google Scholar]
  22. C. Gaucherel, F. Campillo, L. Misson, J. Guiot, J.-J. Boreux. Parameterization of a process-based tree-growth model: Comparison of optimization, MCMC and particle filtering algorithms. Environmental Modelling and Software, 23 (2008), No. 10-11, 1280 – 1288. [CrossRef] [Google Scholar]
  23. C. Godin, H. Sinoquet. Functional-strucutural plant modelling. New Phytologist, 166 (2005), 705–708. [Google Scholar]
  24. G.C. Goodwin, R.L. Payne. Dynamic system identification: Experiment design and data analysis. Academic Press, New York, 1977. [Google Scholar]
  25. Y. Guédon, D. Barthélémy, Y. Caraglio, E. Costes. Pattern analysis in branching and axillary flowering sequences. Journal of Theoretical Biology, 212 (2001), 481–520. [Google Scholar]
  26. H. Guo, V. Letort, L. Hong, T. Fourcaud, P.-H. Cournède, Y. Lu, P. de Reffye. Adaptation of the Greenlab model for analyzing sink-source relationships in Chinese pine saplings. Plant growth Modeling, simulation, visualization and their Applications (PMA06). (T. Fourcaud and XP. Zhang, eds.), IEEE Computer Society, Los Alamitos, California, 2007. [Google Scholar]
  27. Y. Guo, Y.T. Ma, Z.G. Zhan, B.G. Li, M. Dingkuhn, D. Luquet, P. de Reffye. Parameter optimization and field validation of the functional-structural model Greenlab for Maize. Annals of Botany, 97 (2006), 217–230. [CrossRef] [PubMed] [Google Scholar]
  28. F. Hallé, R.A.A. Oldeman. Essai sur l’architecture et la dynamique de croissance des arbres tropicaux. Masson, Paris, 1970. [Google Scholar]
  29. G. Hammer, M. Cooper, F. Tardieu, S. Welch, B. Walsh, F. Van Eeuwijk, S. Chapman, D. Podlich. Models for navigating biological complexity in breeding improved crop plants. Trends in Plant Science, 11 (2006), No. 12, 587–593. [CrossRef] [PubMed] [Google Scholar]
  30. E. Heuvelink. Evaluation of a dynamic simulation model for tomato crop growth and development. Annals of Botany, 83 (1999), 413–422. [CrossRef] [Google Scholar]
  31. C.A. Jones, J.R. Kiniry. Ceres - Maize : A simulation model of Maize growth and development. Texas A&M University Press, 1986. [Google Scholar]
  32. S. Julier, J. Uhlmann, H.F. Durrant-Whyte. A new method for the nonlinear transformation of means and covariances in filters and estimators. IEEE Transactions on Automatic Control, 45 (2000), No. 3, 477–482. [Google Scholar]
  33. M.Z. Kang, P.-H. Cournède, P. de Reffye, D. Auclair, B.G. Hu. Analytical study of a stochastic plant growth model: application to the Greenlab model. Mathematics and Computers in Simulation, 78 (2008), No. 1, 57–75. [CrossRef] [MathSciNet] [Google Scholar]
  34. S. Kirkpatrick, C.D. Gelatt, M.P. Vecchi. Optimization by Simulated Annealing. Science, 220 (1983), No. 4598, 671–680. [NASA ADS] [CrossRef] [MathSciNet] [PubMed] [Google Scholar]
  35. A. Lacointe. Carbon allocation among tree organs : A review of basic processes and representation in functional-structural models. Annals of Forest Sciences, 57 (2000), 521–533. [CrossRef] [EDP Sciences] [Google Scholar]
  36. S. Lemaire, F. Maupas, P.-H. Cournède, P. de Reffye. A morphogenetic crop model for sugar-beet (Beta Vulgaris l.). Crop Modeling and Decision Support, (W. Cao, J. White, E. Wang, eds.), Springer, 2009, 116–129. [Google Scholar]
  37. V. Letort. Multi-scale analysis of source-sink relationships in plant growth models for parameter identification. Case of the Greenlab model. Ph.D. thesis, Ecole Centrale Paris, 2008. [Google Scholar]
  38. V. Letort, P.-H. Cournède, A. Mathieu, P. de Reffye, T. Constant. Parametric identification of a functional-structural tree growth model and application to beech trees (Fagus Sylvatica). Functional Plant Biology, 35 (2008), No. 10, 1243–1254. [CrossRef] [Google Scholar]
  39. V. Letort, P. Mahe, P.-H. Cournède, P. de Reffye, B. Courtois. Quantitative genetics and functional-structural plant growth models: Simulation of quantitative trait loci detection for model parameters and application to potential yield optimization. Annals of Botany, 101 (2008), No. 8, 951–963. [Google Scholar]
  40. L. Ljung. Asymptotic behavior of the extended Kalman filter as a parameter estimator for linear systems. IEEE Trans. Autom. Control, 20 (1979), No. 5, 643–652. [CrossRef] [Google Scholar]
  41. L. Ljung. System identification: Theory for the user. Prentice-Hall, New Jersey, 1999. [Google Scholar]
  42. L. Ljung, T. Söderström. Theory and practice of recursive identification. MIT Press, Cambridge, MA, 1983. [Google Scholar]
  43. C. Loi, P.-H. Cournède. Generating functions of stochastic L-systems and application to models of plant development. Discrete Mathematics and Theoretical Computer Science Proceedings, AI (2008), 325–338. [Google Scholar]
  44. C. Loi, P.-H. Cournède, J. Françon. A symbolic method to analyse patterns in plant structure. Journal of Computer Science and Technology, In Press (2011). [Google Scholar]
  45. D. Luquet, M. Dingkuhn, H. Kim, L. Tambour, A. Clément-Vidal. Ecomeristem, a model of morphogenesis and competition among sinks in rice. 1. concept, validation and sensitivity analysis. Functional Plant Biology, 33 (2007), No. 4, 309–323. [Google Scholar]
  46. Y.T. Ma, M.P. Wen, B.G. Li, P.-H. Cournède, P. de Reffye. Calibration of Greenlab model for Maize with sparse experimental data. The Second International Symposium on Plant Growth Modeling, Simulation, Visualization and Applications (PMA06) (T. Fourcaud and X.P. Zhang, eds.), IEEE, Los Alamitos, California, 2007. [Google Scholar]
  47. D. Makowski, J. Hillier, D. Wallach, B. Andrieu, M.-H. Jeuffroy. Parameter estimation for crop models. Working with Dynamic Crop Models, (D. Wallach, D. Makowski, J.W. Jones, eds.), Elsevier, 2006, 55–100. [Google Scholar]
  48. A. Mathieu, P.-H. Cournède, V. Letort, D. Barthélémy, P. de Reffye. A dynamic model of plant growth with interactions between development and functional mechanisms to study plant structural plasticity related to trophic competition. Annals of Botany, 103 (2009), 1173–1186. [CrossRef] [PubMed] [Google Scholar]
  49. A. Mathieu, B.G. Zhang, E. Heuvelink, S.J. Liu, P.-H. Cournède, P. de Reffye. Calibration of fruit cyclic patterns in cucumber plants as a function of source-sink ratio with the Greenlab model. Proceedings of the 5th international workshop on FSPM (P. Prusinkiewicz, J. Hanan, eds.), November 2007. [Google Scholar]
  50. E. Nicolini, B. Chanson, F. Bonne. Stem growth and epicormic branch formation in understorey beech trees (Fagus Sylvatica l.). Annals of Botany, 87 (2001), 737–750. [CrossRef] [Google Scholar]
  51. J. Olsson, O. Cappé, R. Douc, E. Moulines. Sequential Monte Carlo smoothing with application to parameter estimation in nonlinear state space models. Bernoulli, 14 (2008), No. 1, 155–179. [CrossRef] [MathSciNet] [Google Scholar]
  52. B. Pallas, A. Christophe, P.-H. Cournède, J. Lecoeur. Using a mathematical model to evaluate the trophic and non-trophic determinants of axis development in Grapevine (Vitis Vinifera l.). Functional Plant Biology, 36 (2009), No. 2, 156–170. [CrossRef] [Google Scholar]
  53. P. Prusinkiewicz. Modeling plant growth and development. Current Opinion in Plant Biology, 7 (2004), No. 1, 79–84. [Google Scholar]
  54. M. Quach, N. Brunel, F. d’Alché Buc. Estimating parameters and hidden variables in non-linear state-space models based on ODEs for biological networks inference. Bioinformatics, 23 (2007), No. 23, 3209–3216. [CrossRef] [PubMed] [Google Scholar]
  55. L.R. Rabiner. A tutorial on hidden Markov models and selected applications in speech recognition. Proc. IEEE, 77 (1989), 257–284. [Google Scholar]
  56. O. Schabenberger, F. Pierce. Contemporary statistical models for the plant and soil sciences. Addison-Wesley, 2001. [Google Scholar]
  57. R. Sievänen, E. Nikinmaa, P. Nygren, H. Ozier-Lafontaine, J. Perttunen, H. Hakula. Components of a functional-structural tree model. Annals of Forest Sciences, 57 (2000), 399–412. [CrossRef] [EDP Sciences] [Google Scholar]
  58. W. Taylor. Small sample properties of a class of two-stage Aitken estimator. Econometrica, 45 (1977), No. 2, 497–508. [CrossRef] [MathSciNet] [Google Scholar]
  59. B Thiébaut, S. Puech. Développement du hêtre commun. Morphologie et architecture de l’arbre. Revue Forestière Française, 56 (1984), No. 1, 45–58. [Google Scholar]
  60. E. Walter, L. Pronzato. Identification de modèles paramétriques. Masson, Paris, 2006. [Google Scholar]
  61. F. Wang, M.Z. Kang, Q. Lu, H. Han, V. Letort, Y. Guo, P. de Reffye, B. Li. Simulation of the structure and function of young Mongolian scots pine trees using the stochastic Greenlab model. Annals of Botany, In press (2010). [Google Scholar]
  62. J. Warren-Wilson. Ecological data on dry matter production by plants and plant communities. The collection and processing of field data (E.F. Bradley, O.T. Denmead, eds.), Interscience Publishers, New York, 1967, 77–123. [Google Scholar]
  63. P. Wernecke, J. Müller, T. Dornbusch, A. Wernecke, W. Diepenbrock. The virtual crop modelling system VICA specified for Barley. Functional-structural plant modelling in crop production, (J. Vos, L.F.M. Marcelis, P.H.B. de Visser, P.C. Struik, J.B. Evers, eds.), Springer, 2007, 58–69. [Google Scholar]
  64. Q. Wu, J. Bertheloot, A. Mathieu, B. Andrieu, P.-H. Cournède. Assessment of non-linearity in functional-structural plant models. 6th international workshop on Functional-Structural Plant Models (FSPM10), Davis, USA (T. De Jong, J. Vos, A. Escobar, eds.), November 9-12 2010. [Google Scholar]
  65. Q. Wu, P.-H. Cournède. Sensitivity analysis of Greenlab model for Maize. 3rd international symposium on Plant Growth and Applications(PMA09), Beijing, China (B.G. Li, M. Jaeger, Y. Guo, eds.), IEEE, November 9-12 2009. [Google Scholar]
  66. H.P. Yan, M.Z. Kang, P. de Reffye, M. Dingkuhn. A dynamic, architectural plant model simulating resource-dependent growth. Annals of Botany, 93 (2004), 591–602. [CrossRef] [PubMed] [Google Scholar]
  67. Z.G. Zhan, P. de Reffye, F. Houllier, B.G. Hu. Fitting a structural-functional model with plant architectural data. Plant Growth Models and Applications (B.G. Hu, M. Jaeger, eds.), Tsinghua University Press and Springer (Beijing, China), 2003, 236–249. [Google Scholar]
  68. W. Zucchini, I.L. MacDonald. Hidden Markov models for time series - An introduction using R. Chapman and Hall, 2009. [Google Scholar]

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