Predicting the net carbon exchanges of crop rotations in Europe with an agro-ecosystem model

Carbon fluxes between croplands and atmosphere are highly conditioned by farmer practices that involved intense atmospheric CO2 uptake during crop growing season compared to other terrestrial ecosystems. Modelling and measuring land-atmosphere carbon exchanges from arable lands are important tasks to predict the influence of vegetation dynamics on climate change and its retroactive effects on crop productivity. We tested the agro-ecosystem model CERES-EGC against gap-filled daily net CO2 exchanges over crop rotations monitored in three arable sites in Europe. The model parameters were estimated using Bayesian calibration and the model prediction accuracy was assessed with two supplementary independent data sets. As a result, the calibrated model allowed us to compute the net ecosystem production (NEP) and net biome production (NBP) for entire crop rotations. The Bayesian calibration method results in an improvement of goodness of fit compared to initial parameter-based simulations. The calibrated model was accurate to estimate the NEP from daily time scale to aggregated NEP for entire crop rotation. The carbon returns from application of organic manure and the carbon uptake from catch crops and crop volunteers generated an important C sink effect on the NBP. Adding the nitrous oxide and methane fluxes from soils to the CO2 balance will allow us to compute the total greenhouse gas budget of agro-ecosystems.

Research highlights▶ Carbon fluxes between croplands and atmosphere are highly conditioned by farmer practices that involved intense atmospheric CO2 uptake during crop growing season. ▶ The Bayesian calibration of model parameters results in an improvement of goodness of fit compared to initial parameter-based simulations. ▶ The CERES-EGC model was accurate to estimate the net carbon exchanges between soil-crop and atmosphere from daily time scale to entire crop rotation.