Using approximate Bayesian computation to infer photosynthesis model parameters

ZENG Ji-Ye1 TAN Zheng-Hong2 SAIGUSA Nobuko1

(1.National Institute for Environmental Studies, Tsukuba 305-8506, Japan)
(2.Department of Environmental Science, Hainan University, Haikou, China 570228)

【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 the parameters estimated based on the APMC optimization were un-biased for predicting the photosynthetic rate. We conclude that APMC has greater advantages in estimating the model parameters than those of the conventional nonlinear regression models.

【Keywords】 Monte Carlo; big-leaf model; Farquhar photosynthesis model; net ecosystem exchange;


【Funds】 National Natural Science Foundation of China (31200347, 31660142)

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(Translated by CHEN YF)


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This Article



Vol 41, No. 03, Pages 378-385

March 2017


Article Outline


  • 1 Methods
  • 2 Results
  • 3 Discussion and conclusion
  • References