Quantification of CO2 fluxes in paddy rice based on the characterization and simulation of CO2 assimilation approaches

Abstract

Quantification of the canopy photosynthesis of crops is essential for elucidation of the effects of environmental changes on CO2 fluxes in agricultural ecosystems and crop productivity. This study was conducted to characterize the CO2 fluxes of paddy rice (Oryza sativa), simulate CO2 assimilation based on the development of a photosynthesis model, and project spatiotemporal variations in CO2 assimilation using a crop model based on remotely sensed data in an effort to identify a link between photosynthetic productivity and accumulation of plant biomass. To perform the research practically under actual farming conditions, we investigated the effects of nitrogen (N) fertilization on canopy photosynthesis of rice grown under two levels of N application. Gross primary productivity (GPP) was calculated using net ecosystem exchange and ecosystem respiration measured in a closed-system canopy chamber. GPP was the highest in the maximum tillering stage and its minimum in the heading stage. The initial slope of the light response curve was similar during the four growth stages observed. The sensitivity of GPP to the amount of chlorophyll in the lower N treatment was higher than that in the optimum N treatment, whereas the GPP and yield in plants in the lower N treatment were lower. The photosynthesis model that was developed simulated CO2 assimilation that had statistically acceptable agreement with the corresponding experimental measurements. In addition, projections of spatiotemporal variations in CO2 assimilation were established using the GRAMI-rice model using remotely sensed data. These results indicated that CO2 fluxes in paddy rice could be well quantified based on measurement and simulation projecting spatiotemporal CO2 assimilation. As most of the information was derived from fields, it is not well organized to form one acceptable scientific streamline, efforts should be made to seek ecological implications through a fusion between at-ground measurements and remote sensing observations via model development.