The ability of crop models to predict soil organic carbon changes in a maize cropping system under contrasting fertilization and residues management: Evidence from a long-term experiment

This study assesses the ability of an ensemble crop models (MME) to predict impacts fertilization and residue management on soil organic carbon (SOC) aboveground biomass (AGB) in a long-term experiment (LTE) based continuous maize cropping systems. Data from LTE Northern Italy were used. Treatments included grain (MG) or silage (MS) maize, fertilized with mineral, cattle slurry, farmyard manure. The MME median resulted best predictor observed values. Models performance was better when simulating MG than MS, for crops treated mineral compared fertilizers. dynamics SOC affected by model used year × residues fertilizer interactions. interaction simulate AGB dynamics. Results showed that can effectively production under contrasting management, thus their potential climate change mitigation. uncertainty simulation is related routines partitioning complexity interactions between factors over time. Highlights - A compiled experiment. performances stand-alone assessed management. multi-model using value variables Improved simulations incorporated into soil, regardless increased time systems fertilization.