A comparison of DNDC and DayCent to evaluate GHG emissions from China’s main cropping systems
- 1Institute of Biological & Environmental Science, University of Aberdeen, 23 St Machar Drive, Aberdeen AB24 3UU, UK
- 2State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China
- 3Jiangsu Key Laboratory of Low Carbon Agriculture and GHGs Mitigation, College of Resources and Environmental Sciences, Nanjing Agricultural University, Nanjing, China
- 4Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, School of Environmental Science and Engineering, Nanjing University of Information Science & Technology
- 5Key Laboratory of Arable Land Conservation (Middle and Lower Reaches of the Yangtze River), Ministry of Agriculture, College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, China
- 6College of Agriculture, Guangxi University, Nanning, 530004, China
- 7Beijing Key Laboratory of Biodiversity and Organic Farming, College of Resources and Environmental Sciences, China Agricultural University, Beijing 100193, China
- 8National Engineering Research Center for Wheat, State Key Laboratory of Wheat and Maize Crop Science, Collaborative Innovation Center of Henan Grain Crops, Henan Agricultural University, Zhengzhou, 450046 PR China
China contributes the largest share of cropland’s greenhouse gas (GHG) emissions globally. Processed-based biogeochemical models are useful tools to simulate GHG emissions from cropping systems. However, model comparisons are necessary to provide information for the application of models under different climate, soil, and crop conditions. In this study, two widely-used models (DayCent and DNDC) were evaluated and compared under four main cropping systems in China. The field observations from nine experiments were used for model calibration and validation. The DayCent and DNDC models simulated daily and seasonal CH4 emissions from early rice-late rice and rice-wheat cropping systems reasonably well (r2≥0.49 for daily simulation and nRMSE≤52.9% for seasonal simulation). Both models were able to satisfactorily predict seasonal N2O emissions from maize-wheat fields (0.6≤d≤0.8), but overestimated most daily N2O fluxes at fertilisation and irrigation events. Significantly positive relationships were found between simulated and observed cumulative N2O fluxes in spring maize growing season (0.61≤ r2≤0.85). The DNDC showed smaller differences in simulated and observed cumulative GHG emissions for spring maize and double rice, while DayCent showed better performance on estimating N2O and CH4 for maize-wheat and rice-wheat. This study shows that both models have strengths and weaknesses under a variety of cropping systems and growing regions, which are important to consider when choosing a model for a crop/region-specific simulation.
How to cite: Wang, J., Kuhnert, M., Abdalla, M., Smith, P., Ding, W., Yan, X., Zou, J., Guo, S., Fan, J., Jiang, Y., Hu, R., Li, F., Guo, Y., Chen, Z., Zhao, X., and Xie, Y.: A comparison of DNDC and DayCent to evaluate GHG emissions from China’s main cropping systems, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-9893, https://doi.org/10.5194/egusphere-egu23-9893, 2023.
