Simulating greenhouse gas mitigation potentials for Chinese Croplands using the DAYCENT ecosystem model.

Understanding the potential for greenhouse gas (GHG) mitigation in agricultural lands is a critical challenge for climate change policy. This study uses the DAYCENT ecosystem model to predict GHG mitigation potentials associated with soil management in Chinese cropland systems. Application of ecosystem models, such as DAYCENT, requires the evaluation of model performance with data sets from experiments relevant to the climate and management of the study region. DAYCENT was evaluated with data from 350 cropland experiments in China, including measurements of nitrous oxide emissions (N2 O), methane emissions (CH4 ), and soil organic carbon (SOC) stock changes. In general, the model was reasonably accurate with R(2) values for model predictions vs. measurements ranging from 0.71 to 0.85. Modeling efficiency varied from 0.65 for SOC stock changes to 0.83 for crop yields. Mitigation potentials were estimated on a yield basis (Mg CO2 -equivalent Mg(-1) Yield). The results demonstrate that the largest decrease in GHG emissions in rainfed systems are associated with combined effect of reducing mineral N fertilization, organic matter amendments and reduced-till coupled with straw return, estimated at 0.31 to 0.83 Mg CO2 -equivalent Mg(-1) Yield. A mitigation potential of 0.08 to 0.36 Mg CO2 -equivalent Mg(-1) Yield is possible by reducing N chemical fertilizer rates, along with intermittent flooding in paddy rice cropping systems.