[Estimation of CO2 fluxes from rice paddies based on transparent chamber measurement]

Closed chamber provide a valuable tool for measuring CO2 exchange fluxes. In general, the change rate of CO2 concentration is assumed to be constant in the short measurement time and a linear regression method is used to estimate the CO2 fluxes. However, due to the physical and physiological effects, the change rate of CO2 is not always constant. A linear regression method and an exponential regression method are compared in this study. Results show that during the growing stages except for the ripening, nonlinear relationship of CO2 concentration versus time was estimated in the sunny daytime. Absolute values of CO2 fluxes calculated by linear regression were lower than that by exponential regression. Whereas, CO2 concentration changed linearly with time in cloudy days or in nighttime. And no significant difference was found between the results calculated with these two methods. Accumulative CO2-C fluxes with exponential and linear regressions were compared with the values of net ecosystem exchange of CO2-C (NEE), which were calculated by net primary production (NPP) minus soil respiration. The values with the exponential regression method were closer to NEE than those with linear regression. Therefore, the linear regression method could result in underestimation of carbon budget of ecosystem. Based on transparent chamber measurement, the exponential regression is more feasible to calculate CO2 fluxes than the linear regression.