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  Soil organic carbon models need independent time-series validation for reliable prediction

  Numerical models are crucial to understand and/or predict past and future soil organic carbon dynamics. For those models aiming at prediction, validation is a critical step to gain confidence in projections. With a comprehensive review of ~250 models, we assess how models are validated depending on their objectives and features, discuss how validation of predictive models can be improved. We find a critical lack of independent validation using observed time series. Conducting such validations should be a priority to improve the model reliability. Approximately 60% of the models we analysed are not designed for predictions, but rather for conceptual understanding of soil processes. These models provide important insights by identifying key processes and alternative formalisms that can be relevant for predictive models. We argue that combining independent validation based on observed time series and improved information flow between predictive and conceptual models will increase reliability in predictions.
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  Initial soil organic carbon stocks govern changes in soil carbon: Reality or artifact?

  Changes in soil organic carbon (SOC) storage have the potential to affect global climate; hence identifying environments with a high capacity to gain or lose SOC is of broad interest. Many cross-site studies have found that SOC-poor soils tend to gain or retain carbon more readily than SOC-rich soils. While this pattern may partly reflect reality, here we argue that it can also be created by a pair of statistical artifacts. First, soils that appear SOC-poor purely due to random variation will tend to yield more moderate SOC estimates upon resampling and hence will appear to accrue or retain more SOC than SOC-rich soils. This phenomenon is an example of regression to the mean. Second, normalized metrics of SOC change—such as relative rates and response ratios—will by definition show larger changes in SOC at lower initial SOC levels, even when the absolute change in SOC does not depend on initial SOC. These two artifacts create an exaggerated impression that initial SOC stocks are a major control on SOC dynamics. To address this problem, we recommend applying statistical corrections to eliminate the effect of regression to the mean, and avoiding normalized metrics when testing relationships between SOC change and initial SOC. Careful consideration of these issues in future cross-site studies will support clearer scientific inference that can better inform environmental management.
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  Advancing the mechanistic understanding of the priming effect on soil organic matter mineralisation

  The priming effect (PE) is a key mechanism contributing to the carbon balance of the soil ecosystem. Almost 100 years of research since its discovery in 1926 have led to a rich body of scientific publications to identify the drivers and mechanisms involved. A few review articles have summarised the acquired knowledge; the last major one was published in 2010. Since then, knowledge on the soil microbial communities involved in PE and in PE + C sequestration mechanisms has been considerably renewed. This article reviews current knowledge on soil PE to state to what extent new insights may improve our ability to understand and predict the evolution of soil C stocks. We propose a framework to unify the different concepts and terms that have emerged from the international scientific community on this topic, report recent discoveries and identify key research needs. Seventy per cent of the studies on the soil PE were published in the last 10 years, illustrating a renewed interest for PE, probably linked to the increased concern about the importance of soil carbon for climate change and food security issues. Among all the drivers and mechanisms proposed along with the different studies to explain PE, some are named differently but actually refer to the same object. This overall introduces ‘artificial’ complexity for the mechanistic understanding of PE, and we propose a common, shared terminology. Despite the remaining knowledge gaps, consistent progress has been achieved to decipher the abiotic mechanisms underlying PE, together with the role of enzymes and the identity of the microbial actors involved. However, including PE into mechanistic models of SOM dynamics remains challenging as long as the mechanisms are not fully understood. In the meantime, empirical alternatives are available that reproduce observations accurately when calibration is robust. Based on the current state of knowledge, we propose different scenarios depicting to what extent PE may impact ecosystem services under climate change conditions. Read the free Plain Language Summary for this article on the Journal blog.
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  Nutrients cause consolidation of soil carbon flux to small proportion of bacterial community

  Nutrient amendment diminished bacterial functional diversity, consolidating carbon flow through fewer bacterial taxa. Here, we show strong differences in the bacterial taxa responsible for respiration from four ecosystems, indicating the potential for taxon-specific control over soil carbon cycling. Trends in functional diversity, defined as the richness of bacteria contributing to carbon flux and their equitability of carbon use, paralleled trends in taxonomic diversity although functional diversity was lower overall. Among genera common to all ecosystems, Bradyrhizobium, the Acidobacteria genus RB41, and Streptomyces together composed 45–57% of carbon flow through bacterial productivity and respiration. Bacteria that utilized the most carbon amendment (glucose) were also those that utilized the most native soil carbon, suggesting that the behavior of key soil taxa may influence carbon balance. Mapping carbon flow through different microbial taxa as demonstrated here is crucial in developing taxon-sensitive soil carbon models that may reduce the uncertainty in climate change projections.
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  Long-term afforestation accelerated soil organic carbon accumulation but decreased its mineralization loss and temperature sensitivity in the bulk soils and aggregates

  The conversion of land use from agricultural land to forests is considered an effective measure of mitigating atmospheric CO2, but the impacts of long-term afforestation on soil organic carbon mineralization (Cm) and its temperature sensitivity (Q10) remain uncertain. In this study, we aimed to investigate the effects of different afforestation ages on OC contents and Cm and Q10 in bulk soils and aggregates. Soils were collected from 0–10 cm and 10–20 cm depths in afforested woodlands after 10, 20, 30 and 40 yrs of establishment of Robinia pseudoacacia on abandoned farmlands on the Loess Plateau, China. Cm and Q10 were measured in an 83-day incubation experiment at 25 °C and 15 °C. The results showed that long-term afforestation accelerated soil OC accumulation but decreased its Cm and Q10 in bulk soils and aggregates, and the effects were greater at the 0–10 cm soil depth. Macroaggregates contributed most of the OC content (62%), but microaggregates and silt + clay contributed most of the OC mineralized (40% and 36%) in the bulk soils. The increased OC content and decreased Cm in aggregates suggested an increase in the sequestration of OC in fine soil particles. The temperature sensitivity of OC mineralization increased with increasing particle size, with a higher Q10 value for macroaggregates (1.81 ± 0.44) than for microaggregates (1.42 ± 0.35) and silt + clay (1.31 ± 0.14). Our results indicated that long-term afforestation would be conducive to the accumulation of OC and would decrease the release of CO2 from soils under future climate warming scenarios. The findings highlighted the OC dynamics in abandoned farmland were more sensitive to the temperature changes than those in forests, and the stability of OC in aggregates increased as the aggregate size decreased. This study contributed to bridging current knowledge gapes about the process underlying the observed OC budget and its response to warming scenarios in rehabilitated ecosystems.