Daycent simulation of methane emissions, grain yield, and soil organic carbon in a subtropical paddy rice system

Abstract

The DayCent ecosystem model, widely tested in upland agroecosystems, was recently updated to simulate waterlogged soils. We evaluated the new version in a paddy rice experiment in Southern Brazil. DayCent was used to simulate rice yield, soil organic carbon (SOC), and soil CH4 fluxes. Model calibration was conducted with a multiple-year dataset from the conventional tillage treatment, followed by a validation phase with data from the no-tillage treatment. Model performance was assessed with statistics commonly used in modeling studies: root mean square error (RMSE), model efficiency (EF), and mean difference (M). In general, DayCent slightly underestimated rice yields under no-tillage (by 0.07 Mg ha-1, or 9.2 %) and slightly overestimated soil C stocks, especially in the first years of the experiment. A comparison of observed and simulated CH4 daily fluxes showed that DayCent could simulate the general patterns of soil CH4 fluxes with slight discrepancies. Daily soil CH4 fluxes were overestimated by 0.43 kg ha-1 day-1 (12 %). Growth-season CH4 emissions under no-tillage were also somewhat overestimated (11 % or 45.29 kg ha-1). We conclude that DayCent simulated SOC, rice yield, and CH4 with some inaccuracies, but the overall performance was considered adequate. However, the model failed to represent the observed potential of no-tillage to mitigate CH4 emissions, possibly because model algorithms could not capture the actual field conditions derived from no-tillage management, such as soil redox potential, plant senescence, and surface placement of plant residue.