Purpose Tackling the global carbon deficit through soil organic carbon (SOC) sequestration in agricultural systems has been a focal point in recent years. However, we still lack a comprehensive understanding of actual on-farm SOC sequestration potentials in order to derive effective strategies. Methods Therefore, we chose 21 study sites in North-Eastern Austria covering a wide range of relevant arable soil types and determined SOC pool sizes (0–35 cm soil depth) in pioneer versus conventional management systems in relation to permanently covered reference soils. We evaluated physico-chemical predictors of SOC stocks and SOC quality differences between systems using Fourier-transform infrared (FTIR) spectroscopy. Results Compared to conventional farming systems, SOC stocks were 14.3 Mg ha− 1 or 15.7% higher in pioneer farming systems, equaling a SOC sequestration rate of 0.56 Mg ha− 1 yr− 1. Reference soils however showed approximately 30 and 50% higher SOC stocks than pioneer and conventional farming systems, respectively. Nitrogen and dissolved organic carbon stocks showed similar patterns. While pioneer systems could close the SOC storage deficit in coarse-textured soils, SOC stocks in medium- and fine-textured soils were still 30–40% lower compared to the reference soils. SOC quality, as inferred by FTIR spectra, differed between land-use systems, yet to a lesser extent between cropping systems. Conclusions Innovative pioneer management alleviates SOC storage. Actual realized on-farm storage potentials are rather similar to estimated SOC sequestration potentials derived from field experiments and models. The SOC sequestration potential is governed by soil physico-chemical parameters. More on-farm approaches are necessary to evaluate close-to-reality SOC sequestration potentials in pioneer agroecosystems.

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<p>Soil organic carbon (SOC) constitutes the largest terrestrial biological carbon pool globally. SOC in croplands has declined by approximately 50% since the intensification of agriculture. In light of climate change due to rising greenhouse gas concentrations in the atmosphere, the 4p1000 initiative was launched, suggesting that anthropogenic CO<sub>2</sub> emissions could be offset by increasing SOC stocks in arable land by 0.4% per year by implementing more sustainable agronomic measures. In order to estimate the potential effect of different measures on SOC at the national scale, modelling approaches are required. In the last decades, a wide array of SOC models have been developed and validated for different soils, climate conditions and land uses across the globe. These models all have their own advantages, disadvantages, and sources of uncertainty. Carbon inputs into soil, a major driver of SOC dynamics, are an estimated quantity in all modelling procedures and represent an additional, large source of uncertainty. To reduce uncertainties, multi-model ensembles are suggested to outperform single model runs. The objective of this study is to determine the optimal SOC model ensemble to reduce estimation errors in future studies.</p><p>Therefore, a combination of four carbon turnover models (RothC, Yasso07, ICBM, and C-TOOL) and five published carbon input estimation methods was evaluated by comparing simulations to experimental data from six long-term experiments with 56 treatments on arable land in Austria, with durations from 10 to 32 years to obtain a possible optimal combination for future SOC modelling studies in Austria. Evaluation of model prediction was performed by calculating the absolute mean error (AME), Root Square Mean Error (RMSE) and coefficient of determination on yearly SOC changes to eliminate the effect of different experimental durations on model evaluation.</p><p>We show that obtained models strongly differ in their stock estimates, and our selected ensemble strongly improved the estimations of SOC against single model runs with significantly lower absolute mean errors and root mean square error. This is in accordance with literature results and presents a way forward towards a more accurate modelling. We thus argue that multi-model ensembles to estimate SOC stocks in arable soils in Austria should be preferred over single-model approaches due to improved accuracy.</p>