IDEAS home Printed from https://ideas.repec.org/a/spr/jagbes/v27y2022i1d10.1007_s13253-021-00468-w.html
   My bibliography  Save this article

Bayesian Estimation for the GreenLab Plant Growth Model with Deterministic Organogenesis

Author

Listed:
  • D. Logothetis

    (National and Kapodistrian University of Athens)

  • S. Malefaki

    (University of Patras)

  • S. Trevezas

    (National and Kapodistrian University of Athens)

  • P.-H. Cournède

    (CentraleSupéle, MAS)

Abstract

Plant growth modeling has attracted a lot of attention due to its potential applications. Many scientific disciplines are involved, and a lot of research effort and intensive computer methods were needed to understand better the complex mechanisms underlying plant evolution. Among the numerous challenges, one can cite mathematical modeling, parameterization, estimation and prediction. One of the most promising models that have been proposed in the literature is the GreenLab functional–structural plant growth model. In this study, we focus only on one of its versions, named GreenLab-1, particularly adapted to a certain class of plants with known organogenesis, such as sugar beet, maize, rapeseed and other crop plants. The parameters of the model are related to plant functioning, and the vector of observations consists of organ masses measured only once at a given observation time. Previous efforts for parameter estimation in GreenLab-1 include Kalman-type filters, stochastic variants of EM and/or ECM algorithms, and hybrid sequential importance sampling algorithms with Bayesian estimation only for the functional parameters of the model. In this paper, the first purely Bayesian approach for parameter estimation of the GreenLab-1 model is proposed. This approach has much more flexibility in handling complex structures, thus providing a useful tool for analyzing such types of models. In order to sample from the posterior distribution an MCMC algorithm is used and its implementation issues are also discussed. The performance of this method is illustrated on a simulated and a real dataset from the sugar beet plant, and a comparison is made with the MLE approach.

Suggested Citation

  • D. Logothetis & S. Malefaki & S. Trevezas & P.-H. Cournède, 2022. "Bayesian Estimation for the GreenLab Plant Growth Model with Deterministic Organogenesis," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 27(1), pages 63-87, March.
    • Handle: RePEc:spr:jagbes:v:27:y:2022:i:1:d:10.1007_s13253-021-00468-w
    DOI: 10.1007/s13253-021-00468-w
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13253-021-00468-w
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s13253-021-00468-w?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. 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. Trevezas, S. & Malefaki, S. & Cournède, P.-H., 2014. "Parameter estimation via stochastic variants of the ECM algorithm with applications to plant growth modeling," Computational Statistics & Data Analysis, Elsevier, vol. 78(C), pages 82-99.
    3. Balakrishnan, N. & Ling, M.H., 2012. "EM algorithm for one-shot device testing under the exponential distribution," Computational Statistics & Data Analysis, Elsevier, vol. 56(3), pages 502-509.
    4. Kang, M.Z. & Cournède, P.H. & de Reffye, P. & Auclair, D. & Hu, B.G., 2008. "Analytical study of a stochastic plant growth model: Application to the GreenLab model," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 78(1), pages 57-75.
    5. Yuting Chen & Samis Trevezas & Paul-Henry Cournède, 2015. "A Regularized Particle Filter EM Algorithm Based on Gaussian Randomization with an Application to Plant Growth Modeling," Methodology and Computing in Applied Probability, Springer, vol. 17(4), pages 847-870, December.
    Full references (including those not matched with items on IDEAS)

    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. Wu, Lin & Le Dimet, François-Xavier & de Reffye, Philippe & Hu, Bao-Gang & Cournède, Paul-Henry & Kang, Meng-Zhen, 2012. "An optimal control methodology for plant growth—Case study of a water supply problem of sunflower," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 82(5), pages 909-923.
    2. Trevezas, S. & Malefaki, S. & Cournède, P.-H., 2014. "Parameter estimation via stochastic variants of the ECM algorithm with applications to plant growth modeling," Computational Statistics & Data Analysis, Elsevier, vol. 78(C), pages 82-99.
    3. N. Balakrishnan & E. Castilla & N. Martín & L. Pardo, 2019. "Robust estimators for one-shot device testing data under gamma lifetime model with an application to a tumor toxicological data," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 82(8), pages 991-1019, November.
    4. 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.
    5. Yuting Chen & Samis Trevezas & Paul-Henry Cournède, 2015. "A Regularized Particle Filter EM Algorithm Based on Gaussian Randomization with an Application to Plant Growth Modeling," Methodology and Computing in Applied Probability, Springer, vol. 17(4), pages 847-870, December.
    6. Zhu, Xiaojun & Liu, Kai & He, Mu & Balakrishnan, N., 2021. "Reliability estimation for one-shot devices under cyclic accelerated life-testing," Reliability Engineering and System Safety, Elsevier, vol. 212(C).
    7. Balakrishnan, N. & Ling, M.H., 2014. "Gamma lifetimes and one-shot device testing analysis," Reliability Engineering and System Safety, Elsevier, vol. 126(C), pages 54-64.
    8. Zhu, Xiaojun & Balakrishnan, N., 2022. "One-shot device test data analysis using non-parametric and semi-parametric inferential methods and applications," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    9. Parsons, Russell A. & Mell, William E. & McCauley, Peter, 2011. "Linking 3D spatial models of fuels and fire: Effects of spatial heterogeneity on fire behavior," Ecological Modelling, Elsevier, vol. 222(3), pages 679-691.
    10. Deepak Prajapati & Man Ho Ling & Ping Shing Chan & Debasis Kundu, 2023. "Misspecification of copula for one-shot devices under constant stress accelerated life-tests," Journal of Risk and Reliability, , vol. 237(4), pages 725-740, August.
    11. Balakrishnan, N. & So, H.Y. & Ling, M.H., 2015. "EM algorithm for one-shot device testing with competing risks under exponential distribution," Reliability Engineering and System Safety, Elsevier, vol. 137(C), pages 129-140.
    12. Yao Liu & Yashun Wang & Zhengwei Fan & Xun Chen & Chunhua Zhang & Yuanyuan Tan, 2020. "A new universal multi-stress acceleration model and multi-parameter estimation method based on particle swarm optimization," Journal of Risk and Reliability, , vol. 234(6), pages 764-778, December.

    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:spr:jagbes:v:27:y:2022:i:1:d:10.1007_s13253-021-00468-w. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.