Abstract

<p>Soils represent the largest terrestrial reservoir of organic carbon on land and have the ability to sequester carbon dioxide from the atmosphere. Increasing soil organic carbon (SOC) stocks also improves soil fertility, water holding capacity and prevents erosion. Maintaining SOC stocks is particularly relevant in agricultural soils, where they have been depleted through historical land use. Simulation models representing the dynamics of carbon in the soil are used for predicting the impact of future climate change on SOC dynamics. It is necessary to reduce the uncertainties related to SOC predictions and increase confidence on long-term model simulations. Multi-modeling simulations allow predicting the evolution of SOC stocks, while estimating the uncertainty related to different modeling approaches.</p> <p>In this study, we used a multi-modeling ensemble (ICBM, AMG, RothC and Century) to estimate the amount of carbon inputs required to maintain and increase SOC stocks in 17 agricultural experiments around Europe. Models were run once without calibration and once fitting SOC stocks to long-term observations though parameters’ optimization. Outputs were significantly different among the models and, although no effect of the optimization was found, we observed a significant interaction effect between models and parameters’ optimization. We found that maintaining and increasing SOC stocks is realistic for some experimental conditions, but might be hard to implement at a larger scale.</p>

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