Photosynthetic parameter estimations by considering interactive effects of light, temperature and CO2 concentration

Document Type: Research Paper

Authors

1 Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China.

2 Key Laboratories of Ecosystem Network Observation and Modeling Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China.

Abstract

Biochemical leaf photosynthesis models are evaluated by laboratory results and
have been widely used at field scale for quantification of plant production,
biochemical cycles and land surface processes. It is a key issue to search for
appropriate model structure and parameterization, which determine model
uncertainty. A leaf photosynthesis model that couples the Farquhar-von
Caemmerer-Berry (FvCB) formulation to four different leaf temperature models is
used to investigate the photosynthetic characteristics across a range of temperature
gradients using both light (A-Q) and CO2 response curves (A-Ci). We used the
Bayesian approach to fit the model to trial data of C3 crop plants (soybean, wheat)
in the North China Plain and estimated key photosynthetic parameters, such as the
maximum carboxylation rate of Rubisco (Vcmax25), the potential electron transport
rate (Jmax25), leaf dark respiration in the light (Rd25), mesophyll conductance (gm25)
and the kinetic parameter of Rubisco (Г*25) at a reference temperature of 25 °C.
The results showed that 1) the model with moderate complexity showed the best
goodness of fit, while conversely the simpler and more complex models were
under and over fitting with their corresponding data, respectively; 2) the nonpeaked Arrhenius temperature response, which including both light and CO2
responses data gave the best estimates for the key parameters among the four
models; and 3) the temperature gradient used to verify the model has greatly
improved the estimation of six key parameters (Jmax25, Vcmax25, Rd25, Г*25, Kc25, gm25)
with relatively more narrow confidence intervals (CIs) and showing regular variation on temperature gradient. Overall, this method offers an accurate basis for
estimating leaf photosynthesis parameters and may enhance the accuracy of
canopy, ecosystem and even global vegetation models.




Keywords