Carbon sequestration and soil nitrogen enrichment in Robinia pseudoacacia L. post-mining restoration plantations

Robinia pseudoacacia L. (black locust) has been extensively used for restoring degraded lands, following anthropogenic interventions like coal mining. Here we have addressed the contribution of black locust restoration plantations, established on overburden post-mining material, to carbon storage and to soil nitrogen enrichment at the largest lignite center in Greece. Carbon stocks and fluxes in all pools of the ecosystem, as well as the foliar nitrogen resorption efficiency and soil N stocks were quantified and the effect of plantations’ age was tested. The young age of the plantations (4–24 years) resulted in a relatively low total ecosystem C stock (56.7 t ha−1), which was partitioned among the different pools in the following order: above-ground biomass (50%) > black locust-derived SOC (24%) > coarse roots (14%) > deadwood (6%) > forest floor (5%) > fine roots (less than 1%). Litterfall started early in the growing season and together with fine roots that had a turnover rate of 0.62 yr−1, fueled soil organic carbon. SOC accrual, referring to the accumulation of SOC derived by black locust, declined with age. However, further SOC accumulation is expected, based on the potential SOC storage capacity of soil at the area. C stocks in above- and below-ground biomass increased linearly with age. The same response was observed for soil N stock and NRE, indicating that despite the N2-fixing capacity of black locust, there was still a poor pedospheric N supply and a need for efficient N cycling. Overall, the studied restoration plantations have a considerable contribution to C and N accumulation at the degraded post-mining sites. These positive effects are expected to further increase at least until the plantations reach maturity

The impact of snow cover on nutrients dynamics in Western Siberia territories

International audienc

Modeling soil organic carbon dynamics in temperate forests with Yasso07

In a context of global changes, modeling and predicting the dynamics of soil carbon stocks (CSs) in forest ecosystems are vital but challenging. Yasso07 is considered to be one of the most promising models for such a purpose. We examine the accuracy of its prediction of soil carbon dynamics over the whole French metropolitan territory at a decennial timescale. We used data from 101 sites in the RENECOFOR network, which encompasses most of the French temperate forests. These data include (i) the quantity of above-ground litterfall from 1994 to 2008, measured yearly, and (ii) the soil CSs measured twice at an interval of approximately 15 years (once in the early 1990s and around 2010). We used Yasso07 to simulate the annual changes in carbon stocks (ACCs; in tC ha−1 yr−1) for each site and then compared the estimates with actual recorded data. We carried out meta-analyses to reveal the variability in litter biochemistry in different tree organs for conifers and broadleaves. We also performed sensitivity analyses to explore Yasso07's sensitivity to annual litter inputs and model initialization settings. At the national level, the simulated ACCs (+0.00±0.07 tC ha−1 yr−1, mean ± SE) were of the same order of magnitude as the observed ones (+0.34±0.06 tC ha−1 yr−1). However, the correlation between predicted and measured ACCs remained weak (R2<0.1). There was significant overestimation for broadleaved stands and underestimation for coniferous sites. Sensitivity analyses showed that the final estimated CS was strongly affected by settings in the model initialization, including litter and soil carbon quantity and quality and also by simulation length. Carbon quality set with the partial steady-state assumption gave a better fit than the model with the complete steady-state assumption. With Yasso07 as the support model, we showed that there is currently a bottleneck in soil carbon modeling and prediction due to a lack of knowledge or data on soil carbon quality and fine-root quantity in the litter

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