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Soils account for the largest share of carbon found in terrestrial ecosystems, and their status is of considerable interest for the global carbon cycle budget and atmospheric carbon concentration. The decomposition of soil organic matter depends on environmental conditions and human activities, which raises the question of how permanent are these carbon storages under changing climate. One way to get insight into carbon decomposition processes is to analyse different carbon isotope concentrations in soil organic matter. In this paper we introduce a carbon-13-isotope-specific soil organic matter decomposition add-on into the Yasso soil carbon model and assess its functionality. The new 13 C-dedicated decomposition is straightforward to implement and depends linearly on the default Yasso model parameters and the relative carbon isotope (13 C /12 C) concentration. The model modifications are based on the assumption that the heavier 13 C atoms are not as reactive as 12 C. The new formulations were calibrated using fractionated C, 13 C and δ13 measurements from litterbags containing pine needles and woody material, which were left to decompose in natural environment for 4 years. The introduced model modifications considerably improve the model behaviour in a 100-year-long simulation, where modelled δ13 is compared against fractionated peat column carbon content. The work presented here is a proof of concept and enables 13 C to be used as a natural tracer to detect changes in the underlying soil organic matter decomposition. [ABSTRACT FROM AUTHOR]

Soil organic carbon (SOC) models are important tools for assessing global SOC distributions and how carbon stocks are affected by climate change. Their performances, however, are affected by data and methods used to calibrate them. Here we study how a new version of the Yasso SOC model, here named Yasso20, performs if calibrated individually or with multiple datasets and how the chosen calibration method affects the parameter estimation. We also compare Yasso20 to the previous version of the Yasso model. We found that when calibrated with multiple datasets, the model showed a better global performance compared to a single-dataset calibration. Furthermore, our results show that more advanced calibration algorithms should be used for SOC models due to multiple local maxima in the likelihood space. The comparison showed that the resulting model performed better with the validation data than the previous version of Yasso. [ABSTRACT FROM AUTHOR]

Ecosystems have distinct soil carbon dynamics, including litter decomposition, depending on whether they are dominated by plants featuring ectomycorrhizae (EM) or arbuscular mycorrhizae (AM). However, current soil carbon models treat mycorrhizal impacts on the processes of soil carbon transformation as a black box. We re-formulated the soil carbon model Yasso15 and incorporated impacts of mycorrhizal vegetation on topsoil carbon pools of different recalcitrance. We examined alternative conceptualizations of mycorrhizal impacts on transformations of labile and stable carbon and quantitatively assessed the performance of the selected optimal model in terms of the long-term fate of plant litter 10 years following litter input. We found that mycorrhizal impacts on labile carbon pools are distinct from those on recalcitrant pools. Plant litter of the same chemical composition decomposes slower when exposed to EM-dominated ecosystems compared to AM-dominated ones, and across time, EM-dominated ecosystems accumulate more recalcitrant residues of non-decomposed litter. Overall, adding our mycorrhizal module into the Yasso model improved the accuracy of the temporal dynamics of carbon sequestration predictions. Our results suggest that mycorrhizal impacts on litter decomposition are underpinned by distinct decomposition pathways in AM- and EM-dominated ecosystems. A sensitivity analysis of litter decomposition to climate and mycorrhizal factors indicated that ignoring the mycorrhizal impact on decomposition leads to an overestimation of climate impacts on decomposition dynamics. Our new model provides a benchmark for quantitative modelling of microbial impacts on soil carbon dynamics. It helps to determine the relative importance of mycorrhizal associations and climate on litter decomposition rate and reduces the uncertainties in estimating soil carbon sequestration. [ABSTRACT FROM AUTHOR]

Better monitoring, reporting, and verification (MRV) of the amount, additionality, and persistence of the sequestered soil carbon is needed to understand the best carbon farming practices for different soils and climate conditions, as well as their actual climate benefits or cost efficiency in mitigating greenhouse gas emissions. This paper presents our Field Observatory Network (FiON) of researchers, farmers, companies, and other stakeholders developing carbon farming practices. FiON has established a unified methodology towards monitoring and forecasting agricultural carbon sequestration by combining offline and near-real-time field measurements, weather data, satellite imagery, modeling, and computing networks. FiON's first phase consists of two intensive research sites and 20 voluntary pilot farms testing carbon farming practices in Finland. To disseminate the data, FiON built a web-based dashboard called the Field Observatory (v1.0, https://www.fieldobservatory.org/ , last access: 3 February 2022). The Field Observatory is designed as an online service for near-real-time model–data synthesis, forecasting, and decision support for the farmers who are able to monitor the effects of carbon farming practices. The most advanced features of the Field Observatory are visible on the Qvidja site, which acts as a prototype for the most recent implementations. Overall, FiON aims to create new knowledge on agricultural soil carbon sequestration and effects of carbon farming practices as well as provide an MRV tool for decision support. [ABSTRACT FROM AUTHOR]

Ecosystems dominated by plants featuring ectomycorrhizae (EM) and arbuscular mycorrhizae (AM) promote distinct soil carbon dynamics. AM and EM soil environments can thus have different impacts on litter decomposition. However, current soil carbon models treat mycorrhizal impacts on the processes of soil carbon transformation as a black box. We re-formulated the soil carbon model Yasso15, and incorporated impacts of mycorrhizal vegetation on soil carbon pools of different recalcitrance. We examined alternative conceptualizations of mycorrhizal impacts on transformations of labile and stable carbon, and quantitatively assessed the performance of the selected optimal model in terms of the long-term fate of plant litter. We found that mycorrhizal impacts on pools of labile carbon in the litter are distinct from that on recalcitrant pools. Plant litter of the same chemical composition decomposes slower when exposed to EM-dominated ecosystems compared to AM-dominated ones, and across time, EM-dominated ecosystems accumulate more recalcitrant residues of non-decomposed litter. Overall, adding our mycorrhizal module into the Yasso model improved the accuracy of the temporal dynamics of carbon sequestration predictions. Our results suggest that mycorrhizal impacts on litter decomposition are underpinned by distinct decomposition pathways in AM- and EM-dominated ecosystems. Ignoring mycorrhiza-induced mechanisms will thus lead to an overestimation of climate impacts on decomposition dynamics. Our new model provides a benchmark for mechanistic and quantitative modelling of microbial impact on soil carbon. It helps to determine the relative importance of mycorrhizal associations and climate on organic matter decomposition rate and reduces the uncertainties in estimating soil carbon sequestration. [ABSTRACT FROM AUTHOR]

Soil Organic Carbon (SOC) models are important tools in determining global SOC distributions and how carbon stocks are affected by climate change. Their performances are, however, affected by data and methods used to calibrate them. Here we study how the Yasso SOC model performs if calibrated individually or with multiple datasets and how the chosen calibration method affected the parameter estimation. We found that when calibrated with multiple datasets, the model showed a better global performance compared to a single dataset calibration. Furthermore, our results show that more advanced calibration algorithms should be used for SOC models due to the multiple local maximas in the likelihood space. [ABSTRACT FROM AUTHOR]

Better monitoring, reporting and verification (MRV) of the amount, additionality and persistence of the sequestered soil carbon is needed to understand the best carbon farming practices for different soils and climate conditions, as well as their actual climate benefits or cost-efficiency in mitigating greenhouse gas emissions. This paper presents our Field Observatory Network (FiON) of researchers, farmers, companies and other stakeholders developing carbon farming practices. FiON has established a unified methodology towards monitoring and forecasting agricultural carbon sequestration by combining offline and near real-time field measurements, weather data, satellite imagery, modeling and computing networks. FiON's first phase consists of two intensive research sites and 20 voluntary pilot farms testing carbon farming practices in Finland. To disseminate the data, FiON built a web-based dashboard called Field Observatory (v1.0, fieldobservatory.org). Field Observatory is designed as an online service for near real-time model-data synthesis, forecasting and decision support for the farmers who are able to monitor the effects of carbon farming practices. The most advanced features of the Field Observatory are visible on the Qvidja site which acts as a prototype for the most recent implementations. Overall, FiON aims to create new knowledge on agricultural soil carbon sequestration and effects of carbon farming practices, and provide an MRV tool for decision-support. [ABSTRACT FROM AUTHOR]

Model-calculated forecasts of soil organic carbon (SOC) are important for approximating global terrestrial carbon pools and assessing their change. However, the lack of detailed observations limits the reliability and applicability of these SOC projections. Here, we studied whether state data assimilation (SDA) can be used to continuously update the modeled state with available total carbon measurements in order to improve future SOC estimations. We chose six fallow test sites with measurement time series spanning 30 to 80 years for this initial test. In all cases, SDA improved future projections but to varying degrees. Furthermore, already including the first few measurements impacted the state enough to reduce the error in decades-long projections by at least 1 tCha-1. Our results show the benefits of implementing SDA methods for forecasting SOC as well as highlight implementation aspects that need consideration and further research. [ABSTRACT FROM AUTHOR]

Model-calculated forecasts of soil organic carbon (SOC) are important for approximating global terrestrial carbon pools and assessing their change. However, the lack of detailed observations limits the reliability and applicability of these SOC projections. Here, we studied if state data assimilation (SDA) can be used to continuously update the modeled state with available total carbon measurements in order to improve future SOC estimations. We chose six fallow test sites with measurements time series spanning 30 to 80 years for this initial test. In all cases, SDA improved future projections but to varying degrees. Furthermore, already including the first few measurements impacted the state enough to reduce the error in decades long projections in by at least 1 t C ha−1. Our results show the benefits of implementing SDA methods for forecasting SOC, but also highlight implementation aspects that need consideration and further research. [ABSTRACT FROM AUTHOR]

We investigated the effects of forest management on the carbon (C) dynamics in Romanian forest soils, using two model simulations: CBM-CFS3 and Yasso15. Default parametrization of the models and harmonized litterfall simulated by CBM provided satisfactory results when compared to observed data from National Forest Inventory (NFI). We explored a stratification approach to investigate the improvement of soil C prediction. For stratification on forest types only, the NRMSE (i.e., normalized RMSE of simulated vs. NFI) was approximately 26%, for both models; the NRMSE values reduced to 13% when stratification was done based on climate only. Assuming the continuation of the current forest management practices for a period of 50 years, both models simulated a very small C sink during simulation period (0.05 MgC ha−1 yr−1). Yet, a change towards extensive forest management practices would yield a constant, minor accumulation of soil C, while more intensive practices would yield a constant, minor loss of soil C. For the maximum wood supply scenario (entire volume increment is removed by silvicultural interventions during the simulated period) Yasso15 resulted in larger emissions (−0.3 MgC ha−1 yr−1) than CBM (−0.1 MgC ha−1 yr−1). Under 'no interventions' scenario, both models simulated a stable accumulation of C which was, nevertheless, larger in Yasso15 (0.35 MgC ha−1 yr−1) compared to CBM-CSF (0.18 MgC ha−1 yr−1). The simulation of C stock change showed a strong "start-up" effect during the first decade of the simulation, for both models, explained by the difference in litterfall applied to each scenario compared to the spinoff scenario. Stratification at regional scale based on climate and forest types, represented a reasonable spatial stratification, that improved the prediction of soil C stock and stock change. [ABSTRACT FROM AUTHOR]