Chin J Plan Ecolo ›› 2017, Vol. 41 ›› Issue (3): 378-385.DOI: 10.17521/cjpe.2016.0067

• Method and Technology • Previous Articles    

Using approximate Bayesian computation to infer photosynthesis model parameters

Ji-Ye ZENG1, Zheng-Hong TAN2,*(), Nobuko SAIGUSA1   

  1. 1National Institute for Environmental Studies, Tsukuba 305-8506, Japan
    and
    2Department of Environmental Science, Hainan University, Haikou 570228, China
  • Online:2017-03-10 Published:2017-04-12
  • Contact: Zheng-Hong TAN
  • About author:

    KANG Jing-yao(1991-), E-mail: kangjingyao_nj@163.com

Abstract:

We developed a method, namely Adaptive Population Monte Carlo Approximate Bayesian Computation (APMC), to estimate the parameters of Farquhar photosynthesis model. Treating the canopy as a big leaf, we applied this method to derive the parameters at canopy scale. Validations against observational data showed that parameters estimated based on the APMC optimization are un-biased for predicting the photosynthesis rate. We conclude that APMC has greater advantages in estimating the model parameters than those of the conventional nonlinear regression models.

Key words: Monte Carlo, big-leaf model, Farquhar photosynthesis model, net ecosystem exchange