Modelling greenhouse gas emissions of maize farmland under climate change using DayCent

Atmospheric CO₂ concentration, temperature, and precipitation are the main driving forces of global climate, particularly for the greenhouse gas (GHGs) emissions in the cropland. The response of GHGs emissions to climate change can be of great significance for the carbon emission reduction from farmland. This study aims to evaluate the effect of elevated atmospheric CO₂ concentration on the GHGs emissions from maize farmland. DayCent model was employed to simulate the long-term GHGs emissions. A series of field experiments were also carried out at Changwu National Field Scientific Observation and Research Station of Farmland Ecosystem on the Loess Plateau. The improved open-top chamber systems (OTCs) were then combined to monitor and control the automatic CO₂ concentration. A systematic simulation was performed on the concentration of elevated CO₂ (700 μmol/mol), and the fluxes of N₂O, CH₄, and CO₂ from the maize farmland under natural atmospheric CO₂ concentrations (400 μmol/mol). DayCent model was parameterized using the weather and soil data from the Changwu National Field Scientific Observation and Research Station of Farmland Ecosystem. The DayCent model was calibrated and verified with the “Trial and Error”, according to the two-year observed data on N₂O, CH₄, and CO₂ fluxes. The calibrated model was utilized to explore the GHGs emissions from the maize farmland under low and medium forcing scenarios (Shared Socioeconomic Pathways, SSP126 and SSP245) of Coupled Model Intercomparison Project phase 6 (CMIP6) from 2021 to 2060. The results showed that the simulated fluxes of N₂O, CH₄, and CO₂ with the DayCent model were highly consistent with the observed values under different CO₂ concentrations. Specifically, the model efficiencies (EF) were 0.58-0.87, 0.45-0.65, and 0.25-0.62, respectively, while the root mean square error (RMSE) were 0.83-1.33 g/(hm²·d), 0.67-0.82 g/(hm²·d), and 0.58-0.80 g/(m²·d), respectively, and the coefficients of determination were 0.80-0.91, 0.53-0.80, and 0.53-0.85, respectively. Therefore, the DayCent model can be expected to capture the peaks of N₂O and CO₂ emissions from the farmland after fertilization and precipitation. A better simulation was achieved in the N₂O, CH₄, and CO₂ emissions from the maize cropping systems under different atmospheric CO₂ concentrations. The simulation showed that the elevated CO₂ promoted the GHGs emissions from the farmland. But there was no change in the GHGs emission dynamics during the maize growth period. An increasing trend was found in the air temperature, precipitation, and atmospheric CO₂ concentration under the future climate scenarios of SSP126 and SSP245. Furthermore, there was an increase in the average annual emission rates of greenhouse gas from 2021 to 2060, compared with the period of 2001-2020. The mean annual rates of N₂O and CO₂ emission also increased by 22.8% and 24.9%, while 6.7% and 8.0%, respectively, whereas, the uptake of CH₄ in the maize farmland decreased by 13.6% and 13.4%. A comprehensive analysis demonstrated that the maize farmland can be acted as a source of GHGs emissions under the future climate scenarios of SSP126 and SSP245 in the Loess Plateau. It is a high demand to optimize nitrogen fertilizer management and farmland tillage practices for greenhouse gas emission reduction under future climate change. The finding can provide the basic data support to develop agricultural countermeasures under climate change.