Free Access
Math. Model. Nat. Phenom.
Volume 8, Number 4, 2013
Plant growth modelling
Page(s) 112 - 130
Published online 10 July 2013
  1. D.R. Anderson. Model based inference in the life sciences. Springer, 2008. [Google Scholar]
  2. C. Baey, A. Didier, S. Li, S. Lemaire, F. Maupas, P.-H. Cournède. Evaluation of the predictive capacity of five plant growth models for sugar beet. 4th international symposium on Plant Growth and Applications (PMA12), Shanghai, China, IEEE, 2012. [Google Scholar]
  3. C. Baey, A. Didier, S. Lemaire, F. Maupas, P.-H. Cournède. Modelling the interindividual variability of organogenesis in sugar beet populations using a hierarchical segmented model. Ecological Modelling, 263 (2013), 56–63. [CrossRef] [Google Scholar]
  4. J. Bertheloot, P.-H. Cournède, B. Andrieu. NEMA, a functional-structural model of N economy within wheat culms after flowering: I. Model description. Annals of Botany, 108 (2011), No. 6, 1085–1096. [CrossRef] [PubMed] [Google Scholar]
  5. B.M. Bolker. Ecological models and data in R. Princeton University Press, 2008. [Google Scholar]
  6. 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]
  7. V. Brukkin, N. Morozova. Plant growth and development - basic knowledge and current views. Mathematical Modelling of Natural Phenomena, 6 (2011), No. 2, 1–53. [Google Scholar]
  8. K.P. Burnham, D.R. Anderson. Model selection and multimodel inference: a practical information-theoretic approach. 2nd edition, Springer Verlag, 2002. [Google Scholar]
  9. K. Campbell, M.D. McKay, and B.J. Williams. Sensitivity analysis when model outputs are functions. Reliability Engineering and System Safety, 91 (2006), No. 10-11, 1468–1472. [CrossRef] [Google Scholar]
  10. F. Campillo, V. Rossi. Convolution particle filter for parameter estimation in general state-space models. IEEE Transactions on Aerospace and Electronic Systems, 45 (2009), No. 3, 1063–1072. [CrossRef] [Google Scholar]
  11. F. Campolongo, J. Cariboni, A. Saltelli. An effective screening design for sensitivity analysis of large models. Environmental Modelling and Software, 22 (2007), 1509–518. [CrossRef] [Google Scholar]
  12. O. Cappé, E. Moulines, T. Rydén. Inference in hidden Markov models, Springer, New York, 2005. [Google Scholar]
  13. J. Cariboni, D. Gatelli, R. Liska, A. Saltelli. The role of sensitivity analysis in ecological modelling. Ecological Modelling, 203 (2007), 167–182. [Google Scholar]
  14. E.R. Carson, C. Cobelli. Modelling methodology for physiology and medicine. Academic Press, San Diego (US), 2001. [Google Scholar]
  15. Y. Chen, B. Bayol, C. Loi, S. Trevezas, P.-H. Cournède. Filtrage par noyaux de convolution itératif. Actes des 44èmes Journées de Statistique, JDS2012, Bruxelles 21-25 Mai 2012. [Google Scholar]
  16. P.-H. Cournède. Dynamic system of plant growth. HDR Thesis, University of Montpellier II, 2009. [Google Scholar]
  17. 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]
  18. P.-H. Cournède, V. Letort, A. Mathieu, M.Z. Kang, S. Lemaire, S. Trevezas, F. Houllier, P. de Reffye. Some parameter estimation issues in functional-structural plant modelling. Math. Model. Natural Phenom., 6 (2011), No. 2, 133–159. [CrossRef] [EDP Sciences] [Google Scholar]
  19. D.C. Cox, P. Baybutt. Methods for uncertainty analysis: a comparative survey. Risk Analysis, 1 (1981), No. 4, 251–258. [CrossRef] [Google Scholar]
  20. L. Dente, G. Satalino, F. Mattia, 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, 112 (2008), No. 4, 1395–1407. [CrossRef] [Google Scholar]
  21. 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, pp. 2824–2837. [Google Scholar]
  22. B. Efron, R.J. Tibshirani. An introduction to the bootstrap. Chapman & Hall / CRC Monographs on Statistics and Applied Probability, 1994. [Google Scholar]
  23. G. Evensen. Data assimilation: The ensemble Kalman filter. Springer, 2009. [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. M. Guérif, C. Duke. Calibration of the sucros emergence and early growth module for sugar beet using optical remote sensing data assimilation. European Journal of Agronomy, 9 (1998), 127–136. [CrossRef] [Google Scholar]
  26. M. Guérif, C. Duke. Adjustment procedures of a crop model to the site specific characteristics of soil and crop using remote sensing data assimilation. Agriculture, Ecosystems and Environment, 81 (2000), No. 1, 57–69. [CrossRef] [Google Scholar]
  27. J.C. Helton, J.D. Johnson, C.J. Salaberry, C.B. Storlie. Survey of sampling based methods for uncertainty and sensitivity analysis. Reliability Engineering and System Safety, 91 (2006), 1175–1209. [CrossRef] [Google Scholar]
  28. 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]
  29. Y. Guo, T. Fourcaud, M. Jaeger, X.P. Zhang, B.G. Li. Plant growth and architectural modelling and its applications. Annals of Botany, 107 (2011), 723–727. [CrossRef] [PubMed] [Google Scholar]
  30. R. Hemmerling, O. Kniemeyer, D. Lanwert, G. Buck-Sorlin, W. Kurth. The rule based language XL and the modeling environment GroIMP illustrated with simulated tree competition. Functional Plant Biology 35 (2008), No. 10, 739–750. [CrossRef] [Google Scholar]
  31. T. Homma, A. Saltelli. Importance measures in global sensitivity analysis of nonlinear models. Reliability Engineering and System Safety, 52 (1996), 1–17. [Google Scholar]
  32. C.A. Jones, J.R. Kiniry. Ceres-Maize: A simulation model of Maize growth and development. Texas A&M University Press, 1986. [Google Scholar]
  33. 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]
  34. B.A. Keating, P.S. Carberry, G.L. Hammer, M.E. Probert, M.J. Robertson, D. Holzworth, N.I. Huth, J.N.G. Hargreaves, H. Meinke, Z. Hochman, G. McLean, K. Verburg, V. Snow, J.P. Dimes, M. Silburn, E. Wang, S. Brown, K.L. Bristow, S. Asseng, S. Chapman, R.L. McCown, D.M. Freebairn, C.J. Smith. An overview of APSIM, a model designed for farming systems simulation. European Journal of Agronomy, 18 (2003), No. 3-4, 267–288. [CrossRef] [Google Scholar]
  35. 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]
  36. G. Kitagawa. Monte Carlo filter and smoother for non-gaussian nonlinear state space models. Journal of Computational and Graphical Statistics, 5 (1996), No. 1, 1–25. [MathSciNet] [Google Scholar]
  37. E. Kuhn, M. Lavielle. Maximum likelihood estimation in nonlinear mixed effects models. Computational Statistics and Data Analysis, 49 (2005), No. 4, 1020–1038. [Google Scholar]
  38. M. Lamboni, H. Monod, D. Makowski. Multivariate global sensitivity analysis for dynamic crop models. Field Crops Research, 113 (2009), 312–320. [CrossRef] [Google Scholar]
  39. M. Launay, M. Guérif. Assimilating remote sensing data into a crop model to improve predictive performance for spatial applications. Agriculture, ecosystems and environment, 111 (2005), 321–339. [CrossRef] [Google Scholar]
  40. J. Lecoeur, R. Poiré-Lassus, A. Christophe, B. Pallas, P. Casadebaig, P. Debaeke, F. Vear, L. Guilioni. Quantifying physiological determinants of genetic variation for yield potential in sunflower. SUNFLO: a model-based analysis. Functional Plant Biology, 38 (2011), 246–259. [CrossRef] [Google Scholar]
  41. F. Legland, C. Musso, N. Oudjane. An analysis of regularized interacting particle methods for nonlinear filtering. 3rd IEEE Workshop on Computer-Intensive Methods in Control and Data Processing, Prague, 1998. [Google Scholar]
  42. 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, pp 116–129. [Google Scholar]
  43. S. Lemaire, F. Maupas, P.-H. Cournède, J.-M. Allirand, P. de Reffye, B. Ney. Analysis of the density effects on the source-sink dynamics in sugar-beet growth. 3rd international symposium on Plant Growth and Applications(PMA09), Beijing, China (B.-G. Li, M. Jaeger, Y. Guo, eds.), IEEE Computer Society (Los Alamitos, California), Novem. 9-12 2009. [Google Scholar]
  44. 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]
  45. Y. Ma, M.P. Wen, Y. Guo, B.G. Li, P.-H. Cournède, P. de Reffye. Parameter optimization and field validation of the functional-structural model GreenLab for maize at different population densities. Annals of Bot., 101 (2008), 1185–1194. [CrossRef] [Google Scholar]
  46. 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]
  47. H. Monod, C. Naud, D. Makowski. Uncertainty and sensitivity analysis for crop models. Working with Dynamic Crop Models (D. Wallach, D. Makowski, J.W. Jones, eds.), Elsevier, 2006, pp. 55–100. [Google Scholar]
  48. M.G. Morgan, M. Henrion, M. Small. Uncertainty. Cambridge University Press, 1990. [Google Scholar]
  49. M.D. Morris. Factorial sampling plans for preliminary computational experiments. Technometrics, 33 (1991), 161–174. [Google Scholar]
  50. T. Nilson. A theoretical analysis of the frequency of gaps in plant stands. Agricult. and Forest Meteorol., 8 (1971), 25–38. [CrossRef] [Google Scholar]
  51. A. O’Hagan, J.J. Forster. Kendall’s advanced theory of statistics: Bayesian inference. Arnold, London, 2nd edit., 2004, [Google Scholar]
  52. J. Perttunen, R. Sievänen, E. Nikinmaa, H. Salminen, H. Saarenmaa, J. Vakeva. Incorporating Lindenmayer systems for architectural development in a functional-structural tree model. Ecological Modelling, 181 (2005), 479–491. [CrossRef] [Google Scholar]
  53. C. Pradal, S. Dufour-Kowalski, F. Boudon, C. Fournier, C. Godin. OpenAlea: a visual programming and component-based software platform for plant modelling. Functional Plant Biology, 35 (2008), No. 10, 751–760. [CrossRef] [Google Scholar]
  54. V. Rossi, J.-P. Vila. Nonlinear filtering in discrete time: A particle convolution approach. Annales de l’Institut de Statistique de l’Université de Paris, 50 (2006), No. 3, 71–102. [Google Scholar]
  55. F. Ruget, N. Brisson, R. Delécolle, R. Faivre. Sensitivity analysis of a crop simulation model, STICS, in order to choose the main parameters to be estimated. Agronomie, 22 (2002), 133–158. [CrossRef] [EDP Sciences] [Google Scholar]
  56. A. Saltelli, M. Ratto, T. Andres, F. Campolongo, J. Cariboni, D. Gatelli, M. Saisana, S. Tarantola. Global sensitivity analysis. The primer ed., John Wiley&Sons, 2008. [Google Scholar]
  57. Y.H. Shi, R. Eberhart. A modified particle swarm optimizer. Evolutionary Computation Proceedings (IEEE World Congress on Computational Intelligence) (K.R. Belew, L.B. Booker, eds.), Morgan Kaufmann, 1998, pp. 69–73. [Google Scholar]
  58. I. Sobol. Sensitivity analysis for non-linear mathematical models. Math. Model. Comput. Experim., 1 (1993), 407–414. [Google Scholar]
  59. S. Trevezas, P.-H. Cournède. A sequential Monte Carlo approach for MLE in a plant growth model. Journal of Agricultural, Biological, and Environmental Statistics, 18 (2013), No. 2, 250–270. [Google Scholar]
  60. W. Taylor. Small sample properties of a class of two-stage Aitken estimator. Econometrica, 45 (1977), No. 2, 497–508. [CrossRef] [MathSciNet] [Google Scholar]
  61. R.H. Van Waveren, S. Groot, H. Scholten, F. Van Geer, H. Wosten, R. Koeze, J. Noort. Good modelling practice handbook. Tech. Report 99-05, STOWA, Utrecht, RWS-RIZA, Lelystad, The Netherlands, 1999. [Google Scholar]
  62. H. Varella, S. Buis, M. Launay, and M. Guérif. Global sensitivity analysis for choosing the main soil parameters of a crop model to be determined. Agricultural Sciences, 3 (2012), 949–961. [CrossRef] [Google Scholar]
  63. J. Vos, J.B. Evers, G.H. Buck-Sorlin, B. Andrieu, M. Chelle, P.H.B. de Visser. Functional-structural plant modelling: a new versatile tool in crop science. Journal of Experimental Botany, 61 (2010), No. 8, 2101–2115. [CrossRef] [PubMed] [Google Scholar]
  64. D. Wallach, B. Goffinet. Mean Squared Error of Prediction in Models for Studying Ecological and Agronomic Systems. Biometrics, 43 (1987), No. 3, 561–573. [CrossRef] [Google Scholar]
  65. D. Wallach, B. Goffinet, J.-E. Bergez, P. Debaeke, D. Leenhardt, J.-N. Aubertot. The effect of parameter uncertainty on a model with adjusted parameters. Agronomie, 22 (2002), 159–170. [CrossRef] [EDP Sciences] [Google Scholar]
  66. D. Wallach, S. Buis, P. Lecharpentier, J. Bourges, P. Clastre, M. Launay, J.-E. Bergez, M. Guérif, J. Soudais, E. Justes. A package of parameter estimation methods and implementation for the STICS crop-soil model. Environmental Modelling and Software, 26 (2011), 386–394. [CrossRef] [Google Scholar]
  67. E. Walter, L. Pronzato. Identification de modèles paramétriques. Masson, Paris, 2006. [Google Scholar]
  68. 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]
  69. Q. Wu, P.-H. Cournède, A. Mathieu. An efficient computational method for global sensitivity analysis and its application to tree growth modelling Reliability Engineering and System Safety, 107 (2012), 35–43. [CrossRef] [Google Scholar]
  70. Q. Wu, P.-H. Cournède. A comprehensive methodology of global sensitivity analysis for complex mechanistic models: An application to plant growth. Submitted, (2013). [Google Scholar]
  71. 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]

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