Using approximate Bayesian computation to infer photosynthesis model parameters |
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Affiliation: | National Institute for Environmental Studies, Tsukuba 305-8506, Japan and Department of Environmental Science, Hainan University, Haikou 570228, China |
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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. |
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Keywords: | Monte Carlo, big-leaf model, Farquhar photosynthesis model, net ecosystem exchange |
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