Improving model prediction of soil N₂O emissions through Bayesian calibration

Area of Science:

• Agricultural Science
• Environmental Science
• Soil Science

Background:

• Nitrous oxide (N2O) emissions from arable soils are driven by complex biogeochemical processes.
• Accurate prediction of N2O emissions is crucial for managing agricultural impacts.
• Agroecosystem models are vital tools for simulating N2O emissions but require site-specific calibration.

Purpose of the Study:

• To calibrate key parameters of the Landscape-DNDC agroecosystem model for improved N2O prediction.
• To assess the effectiveness of Bayesian calibration using the Metropolis-Hastings algorithm.
• To evaluate model performance against measured N2O data at multiple arable sites.

Main Methods:

• Bayesian calibration of nine Landscape-DNDC model parameters using the Metropolis-Hastings algorithm.
• Parameter posterior distribution estimation at four distinct arable sites in the UK.
• Validation of calibrated model predictions against independent N2O emission data from ten arable sites.

Main Results:

• Calibration successfully predicted previously unsimulated N2O emission peaks at several sites.
• The calibrated model demonstrated a significant improvement in predicting soil N2O fluxes.
• Overall prediction accuracy for soil N2O fluxes across all sites increased by 33%.

Conclusions:

• Bayesian calibration is an effective method for enhancing the predictive accuracy of agroecosystem models for N2O emissions.
• Site-specific calibration is essential for reliable N2O emission simulations in diverse agricultural settings.
• Improved N2O flux predictions contribute to better environmental management in agriculture.