Predicting Methane Emissions from Paddy Rice Soils under Biochar and Nitrogen Addition Using DNDC Model

Methane (CH4) is a second largest contributor of global warming after carbon dioxide (CO2), and it is crucial to understand how management practices affect CH4 emissions. Among field crops, paddy rice alone has accounted for about 10-12% of the total CH4 emission in 2010. The process-based DeNitrification and DeComposition (DNDC) model can be applied to quantify greenhouse gas (GHG) emissions from agricultural soils. Capturing both the daily time-scale and cumulative growing season CH4 fluxes by DNDC may help to devise appealing mitigation approaches for better rice management. In this study, DNDC was calibrated with a parameter-adjustment approach under two treatments: 140 kg N ha(-1) without biochar and 140 kg N ha(-1) with 2% bio -char. Simulation results show that the model predicted the daily CH4 fluxes in good agreement with measure-ments under both treatments. Next, DNDC was validated with the adjusted parameters against the remaining biochar and N treatments, and the model performed well in prediction of CH4 fluxes as indicated by several statistical indexes: RMSE ranged from 6.74 to 7.62 g CH4 ha(-1)d(-1), the D-index varied between 0.92 to 0.98, and MPD and nRMSE were at 10.94-17.43% and 17.54-24.52%, respectively. In terms of cumulative growing season CH4 efflux, DNDC under-simulated all treatments except the control. Further DNDC predicted above-ground dry weights and volumetric water contents in good agreement with the measurements. Moreover, model poorly predicted soil temperature, pH, and soil moisture content. The value of the D-index varied from 0.12 to 0.38 for both soil temperature and pH. Subsequently, DNDC successfully identified the significant impact of biochar on CH4 emission, and model error was strongly correlated with pH of soils with no biochar amendments. In conclusion, the DNDC model can capture the daily time-scale as well as annual-scale CH4 fluxes, though DNDC must be validated by intensive measurements of additional soil variables, including dissolved organic carbon (DOC) and microbial composition under different biochar types.