Multi-model uncertainty analysis in predicting grain N for crop rotations in Europe

Realistic estimation of grain nitrogen (N; N in grain yield) is crucial for assessing N management in croprotations, but there is little information on the performance of commonly used crop models for simulat-ing grain N. Therefore, the objectives of the study were to (1) test if continuous simulation (multi-year)performs better than single year simulation, (2) assess if calibration improves model performance atdifferent calibration levels, and (3) investigate if a multi-model ensemble can substantially reduce uncer-tainty in reproducing grain N. For this purpose, 12 models were applied simulating different treatments(catch crops, CO2concentrations, irrigation, N application, residues and tillage) in four multi-year rota-tion experiments in Europe to assess modelling accuracy. Seven grain and seed crops in four rotationsystems in Europe were included in the study, namely winter wheat, winter barley, spring barley, springoat, winter rye, pea and winter oilseed rape. Our results indicate that the higher level of calibrationsignificantly increased the quality of the simulation for grain N. In addition, models performed betterin predicting grain N of winter wheat, winter barley and spring barley compared to spring oat, winterrye, pea and winter oilseed rape. For each crop, the use of the ensemble mean significantly reduced the mean absolute percentage error (MAPE) between simulations and observations to less than 15%, thusa multi–model ensemble can more precisely predict grain N than a random single model. Models correctly simulated the effects of enhanced N input on grain N of winter wheat and winter barley, whereas effectsof tillage and irrigation were less well estimated. However, the use of continuous simulation did notimprove the simulations as compared to single year simulation based on the multi-year performance,which suggests needs for further model improvements of crop rotation effects.