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

Area of Science:

• Agricultural Science
• Environmental Science
• Climate Change Research

Background:

• Greenhouse gas (GHG) mitigation in agricultural lands is crucial for climate change policy.
• Chinese cropland systems present significant opportunities for GHG mitigation.
• Ecosystem models require validation with regional experimental data.

Purpose of the Study:

• To predict GHG mitigation potentials in Chinese cropland systems using the DAYCENT ecosystem model.
• To evaluate the performance of the DAYCENT model with Chinese experimental data.
• To quantify mitigation potentials on a yield basis (Mg CO2-equivalent Mg(-1) Yield).

Main Methods:

• Utilized the DAYCENT ecosystem model to simulate GHG emissions and soil organic carbon (SOC) changes.
• Validated the model using data from 350 Chinese cropland experiments, including N2O, CH4, and SOC measurements.
• Estimated mitigation potentials by analyzing the impact of various soil management practices.

Main Results:

• The DAYCENT model demonstrated reasonable accuracy, with R(2) values ranging from 0.71 to 0.85 for predictions versus measurements.
• Modeling efficiency was high, ranging from 0.65 for SOC stock changes to 0.83 for crop yields.
• The most significant GHG emission reductions in rainfed systems (0.31–0.83 Mg CO2-eq Mg(-1) Yield) resulted from combined reduced N fertilization, organic amendments, reduced tillage, and straw return.

Conclusions:

• Combined soil management practices, including reduced nitrogen fertilization, organic matter amendments, reduced tillage, and straw return, offer substantial GHG mitigation potential in Chinese rainfed croplands.
• Reducing N chemical fertilizer rates coupled with intermittent flooding in paddy rice systems also presents a viable mitigation strategy (0.08–0.36 Mg CO2-eq Mg(-1) Yield).
• The DAYCENT model serves as a valuable tool for assessing GHG mitigation strategies in agricultural systems.