IDEAS home Printed from https://ideas.repec.org/a/eee/ecomod/v263y2013icp56-63.html
   My bibliography  Save this article

Modelling the interindividual variability of organogenesis in sugar beet populations using a hierarchical segmented model

Author

Listed:
  • Baey, Charlotte
  • Didier, Anne
  • Lemaire, Sébastien
  • Maupas, Fabienne
  • Cournède, Paul-Henry

Abstract

Modelling the interindividual variability in plant populations is a key issue to enhance the predictive capacity of plant growth models at the field scale. In the case of sugar beet, this variability is well illustrated by rate of leaf appearance, or by its inverse the phyllochron. Indeed, if the mean phyllochron remains stable among seasons, there is a strong variability between individuals, which is not taken into account when using models based only on mean population values.

Suggested Citation

  • Baey, Charlotte & Didier, Anne & Lemaire, Sébastien & Maupas, Fabienne & Cournède, Paul-Henry, 2013. "Modelling the interindividual variability of organogenesis in sugar beet populations using a hierarchical segmented model," Ecological Modelling, Elsevier, vol. 263(C), pages 56-63.
  • Handle: RePEc:eee:ecomod:v:263:y:2013:i:c:p:56-63
    DOI: 10.1016/j.ecolmodel.2013.04.013
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304380013002172
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ecolmodel.2013.04.013?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Wolfinger, Russell D. & Xihong Lin, 1997. "Two Taylor-series approximation methods for nonlinear mixed models," Computational Statistics & Data Analysis, Elsevier, vol. 25(4), pages 465-490, September.
    2. Alexander Shapiro & Jos Berge, 2002. "Statistical inference of minimum rank factor analysis," Psychometrika, Springer;The Psychometric Society, vol. 67(1), pages 79-94, March.
    3. Marc Lavielle & Adeline Samson & Ana Karina Fermin & France Mentré, 2011. "Maximum Likelihood Estimation of Long-Term HIV Dynamic Models and Antiviral Response," Biometrics, The International Biometric Society, vol. 67(1), pages 250-259, March.
    4. Ke C. & Wang Y., 2001. "Semiparametric Nonlinear Mixed-Effects Models and Their Applications," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1272-1298, December.
    5. Kuhn, E. & Lavielle, M., 2005. "Maximum likelihood estimation in nonlinear mixed effects models," Computational Statistics & Data Analysis, Elsevier, vol. 49(4), pages 1020-1038, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Charlotte Baey & Amélie Mathieu & Alexandra Jullien & Samis Trevezas & Paul-Henry Cournède, 2018. "Mixed-Effects Estimation in Dynamic Models of Plant Growth for the Assessment of Inter-individual Variability," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 23(2), pages 208-232, June.
    2. Baey, Charlotte & Didier, Anne & Lemaire, Sébastien & Maupas, Fabienne & Cournède, Paul-Henry, 2014. "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, Elsevier, vol. 290(C), pages 11-20.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Solène Desmée & France Mentré & Christine Veyrat-Follet & Bernard Sébastien & Jérémie Guedj, 2017. "Using the SAEM algorithm for mechanistic joint models characterizing the relationship between nonlinear PSA kinetics and survival in prostate cancer patients," Biometrics, The International Biometric Society, vol. 73(1), pages 305-312, March.
    2. Charlotte Baey & Amélie Mathieu & Alexandra Jullien & Samis Trevezas & Paul-Henry Cournède, 2018. "Mixed-Effects Estimation in Dynamic Models of Plant Growth for the Assessment of Inter-individual Variability," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 23(2), pages 208-232, June.
    3. Fu, Liyong & Wang, Mingliang & Lei, Yuancai & Tang, Shouzheng, 2014. "Parameter estimation of two-level nonlinear mixed effects models using first order conditional linearization and the EM algorithm," Computational Statistics & Data Analysis, Elsevier, vol. 69(C), pages 173-183.
    4. Anastasiou, Andreas, 2017. "Bounds for the normal approximation of the maximum likelihood estimator from m-dependent random variables," Statistics & Probability Letters, Elsevier, vol. 129(C), pages 171-181.
    5. Denter, Philipp & Sisak, Dana, 2015. "Do polls create momentum in political competition?," Journal of Public Economics, Elsevier, vol. 130(C), pages 1-14.
    6. Salgado Alfredo, 2018. "Incomplete Information and Costly Signaling in College Admissions," Working Papers 2018-23, Banco de México.
    7. Albrecht, James & Anderson, Axel & Vroman, Susan, 2010. "Search by committee," Journal of Economic Theory, Elsevier, vol. 145(4), pages 1386-1407, July.
    8. Blier-Wong, Christopher & Cossette, Hélène & Marceau, Etienne, 2023. "Risk aggregation with FGM copulas," Insurance: Mathematics and Economics, Elsevier, vol. 111(C), pages 102-120.
    9. Simon Bruhn & Thomas Grebel & Lionel Nesta, 2023. "The fallacy in productivity decomposition," Journal of Evolutionary Economics, Springer, vol. 33(3), pages 797-835, July.
    10. Wim J. van der Linden, 2019. "Lord’s Equity Theorem Revisited," Journal of Educational and Behavioral Statistics, , vol. 44(4), pages 415-430, August.
    11. Ibirénoyé Romaric Sodjahin & Fabienne Femenia & Obafemi Philippe Koutchade & A. Carpentier, 2022. "On the economic value of the agronomic effects of crop diversification for farmers: estimation based on farm cost accounting data [Valeur économique des effets agronomiques de la diversification de," Working Papers hal-03639951, HAL.
    12. Valdemar Melicher & Tom Haber & Wim Vanroose, 2017. "Fast derivatives of likelihood functionals for ODE based models using adjoint-state method," Computational Statistics, Springer, vol. 32(4), pages 1621-1643, December.
    13. Simar, Léopold & Wilson, Paul, 2022. "Modern Tools for Evaluating the Performance of Health-Care Providers," LIDAM Discussion Papers ISBA 2022006, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    14. Allassonnière, Stéphanie & Kuhn, Estelle, 2015. "Convergent stochastic Expectation Maximization algorithm with efficient sampling in high dimension. Application to deformable template model estimation," Computational Statistics & Data Analysis, Elsevier, vol. 91(C), pages 4-19.
    15. Tasche, Dirk, 2013. "Bayesian estimation of probabilities of default for low default portfolios," Journal of Risk Management in Financial Institutions, Henry Stewart Publications, vol. 6(3), pages 302-326, July.
    16. Diers, Dorothea & Linde, Marc & Hahn, Lukas, 2016. "Addendum to ‘The multi-year non-life insurance risk in the additive reserving model’ [Insurance Math. Econom. 52(3) (2013) 590–598]: Quantification of multi-year non-life insurance risk in chain ladde," Insurance: Mathematics and Economics, Elsevier, vol. 67(C), pages 187-199.
    17. Anastasiou, Andreas, 2017. "Bounds for the normal approximation of the maximum likelihood estimator from m -dependent random variables," LSE Research Online Documents on Economics 83635, London School of Economics and Political Science, LSE Library.
    18. Hirschberg, Joe & Lye, Jenny, 2017. "Inverting the indirect—The ellipse and the boomerang: Visualizing the confidence intervals of the structural coefficient from two-stage least squares," Journal of Econometrics, Elsevier, vol. 199(2), pages 173-183.
    19. Laura Azzimonti & Francesca Ieva & Anna Maria Paganoni, 2013. "Nonlinear nonparametric mixed-effects models for unsupervised classification," Computational Statistics, Springer, vol. 28(4), pages 1549-1570, August.
    20. Serguei Kaniovski & Alexander Zaigraev, 2018. "The probability of majority inversion in a two-stage voting system with three states," Theory and Decision, Springer, vol. 84(4), pages 525-546, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ecomod:v:263:y:2013:i:c:p:56-63. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/ecological-modelling .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.