The term carbon (C) sequestration has not just become a buzzword but is something of a siren's call to scientific communicators and media outlets. Carbon sequestration is the removal of C from the atmosphere and the storage, for example, in soil. It has the potential to partially compensate for anthropogenic greenhouse gas emissions and is, therefore, an important piece in the global climate change mitigation puzzle. However, the term C sequestration is often used misleadingly and, while likely unintentional, can lead to the perpetuation of biased conclusions and exaggerated expectations about its contribution to climate change mitigation efforts. Soils have considerable potential to take up C but many are also in a state of continuous loss. In such soils, measures to build up soil C may only lead to a reduction in C losses (C loss mitigation) rather than result in real C sequestration and negative emissions. In an examination of 100 recent peer‐reviewed papers on topics surrounding soil C, only 4% were found to have used the term C sequestration correctly. Furthermore, 13% of the papers equated C sequestration with C stocks. The review, further, revealed that measures leading to C sequestration will not always result in climate change mitigation when non‐CO₂ greenhouse gases and leakage are taken into consideration. This paper highlights potential pitfalls when using the term C sequestration incorrectly and calls for accurate usage of this term going forward. Revised and new terms are suggested to distinguish clearly between C sequestration in soils, SOC loss mitigation, negative emissions, climate change mitigation, SOC storage, and SOC accrual to avoid miscommunication among scientists and stakeholder groups in future.

Agroforestry is an increasingly popular farming system enabling agricultural diversification and providing several ecosystem services. In agroforestry systems, soil organic carbon (SOC) stocks are generally increased, but it is difficult to disentangle the different factors responsible for this storage. Organic carbon (OC) inputs to the soil may be larger, but SOC decomposition rates may be modified owing to microclimate, physical protection, or priming effect from roots, especially at depth. We used an 18-year-old silvoarable system associating hybrid walnut trees (Juglans regia × nigra) and durum wheat (Triticum turgidum L. subsp. durum) and an adjacent agricultural control plot to quantify all OC inputs to the soil – leaf litter, tree fine root senescence, crop residues, and tree row herbaceous vegetation – and measured SOC stocks down to 2 m of depth at varying distances from the trees. We then proposed a model that simulates SOC dynamics in agroforestry accounting for both the whole soil profile and the lateral spatial heterogeneity. The model was calibrated to the control plot only. Measured OC inputs to soil were increased by about 40 % (+ 1.11 t C ha⁻¹ yr⁻¹) down to 2 m of depth in the agroforestry plot compared to the control, resulting in an additional SOC stock of 6.3 t C ha⁻¹ down to 1 m of depth. However, most of the SOC storage occurred in the first 30 cm of soil and in the tree rows. The model was strongly validated, properly describing the measured SOC stocks and distribution with depth in agroforestry tree rows and alleys. It showed that the increased inputs of fresh biomass to soil explained the observed additional SOC storage in the agroforestry plot. Moreover, only a priming effect variant of the model was able to capture the depth distribution of SOC stocks, suggesting the priming effect as a possible mechanism driving deep SOC dynamics. This result questions the potential of soils to store large amounts of carbon, especially at depth. Deep-rooted trees modify OC inputs to soil, a process that deserves further study given its potential effects on SOC dynamics.

Models of soil organic carbon (SOC) storage and turnover can be useful tools to analyse the effects of soil and crop management practices and climate change on soil organic carbon stocks. The aggregated structure of soil is known to protect SOC from decomposition and, thus, influence the potential for long-term sequestration. In turn, the turnover and storage of SOC affects soil aggregation, physical and hydraulic properties and the productive capacity of soil. These two-way interactions have not yet been explicitly considered in modelling approaches. In this study, we present and describe a new model of the dynamic feedbacks between soil organic matter (SOM) storage and soil physical properties (porosity, pore size distribution, bulk density and layer thickness). A sensitivity analysis was first performed to understand the behaviour of the model. The identifiability of model parameters was then investigated by calibrating the model against a synthetic data set. This analysis revealed that it would not be possible to unequivocally estimate all of the model parameters from the kind of data usually available in field trials. Based on this information, the model was tested against measurements of bulk density, SOC concentration and limited data on soil water retention and soil surface elevation made during 63 years in a field trial located near Uppsala (Sweden) in three treatments with different organic matter (OM) inputs (bare fallow, animal and green manure). The model was able to accurately reproduce the changes in SOC, soil bulk density and surface elevation observed in the field as well as soil water retention curves measured at the end of the experimental period in 2019 in two of the treatments. Treatment-specific variations in SOC dynamics caused by differences in OM input quality could be simulated very well by modifying the value for the OM retention coefficient ε (0.37 for animal manure and 0.14 for green manure). The model approach presented here may prove useful for management purposes, for example, in an analysis of carbon sequestration or soil degradation under land use and climate change.

Changes in global soil carbon stocks have considerable potential to influence the course of future climate change. However, a portion of soil organic carbon (SOC) has a very long residence time ( > 100 years) and may not contribute significantly to terrestrial greenhouse gas emissions during the next century. The size of this persistent SOC reservoir is presumed to be large. Consequently, it is a key parameter required for the initialization of SOC dynamics in ecosystem and Earth system models, but there is considerable uncertainty in the methods used to quantify it. Thermal analysis methods provide cost-effective information on SOC thermal stability that has been shown to be qualitatively related to SOC biogeochemical stability. The objective of this work was to build the first quantitative model of the size of the centennially persistent SOC pool based on thermal analysis. We used a unique set of 118 archived soil samples from four agronomic experiments in northwestern Europe with long-term bare fallow and non-bare fallow treatments (e.g., manure amendment, cropland and grassland) as a sample set for which estimating the size of the centennially persistent SOC pool is relatively straightforward. At each experimental site, we estimated the average concentration of centennially persistent SOC and its uncertainty by applying a Bayesian curve-fitting method to the observed declining SOC concentration over the duration of the long-term bare fallow treatment. Overall, the estimated concentrations of centennially persistent SOC ranged from 5 to 11 g C kg⁻¹ of soil (lowest and highest boundaries of four 95 % confidence intervals). Then, by dividing the site-specific concentrations of persistent SOC by the total SOC concentration, we could estimate the proportion of centennially persistent SOC in the 118 archived soil samples and the associated uncertainty. The proportion of centennially persistent SOC ranged from 0.14 (standard deviation of 0.01) to 1 (standard deviation of 0.15). Samples were subjected to thermal analysis by Rock-Eval 6 that generated a series of 30 parameters reflecting their SOC thermal stability and bulk chemistry. We trained a nonparametric machine-learning algorithm (random forests multivariate regression model) to predict the proportion of centennially persistent SOC in new soils using Rock-Eval 6 thermal parameters as predictors. We evaluated the model predictive performance with two different strategies. We first used a calibration set (n = 88) and a validation set (n = 30) with soils from all sites. Second, to test the sensitivity of the model to pedoclimate, we built a calibration set with soil samples from three out of the four sites (n = 84). The multivariate regression model accurately predicted the proportion of centennially persistent SOC in the validation set composed of soils from all sites (R² = 0.92, RMSEP = 0.07, n = 30). The uncertainty of the model predictions was quantified by a Monte Carlo approach that produced conservative 95 % prediction intervals across the validation set. The predictive performance of the model decreased when predicting the proportion of centennially persistent SOC in soils from one fully independent site with a different pedoclimate, yet the mean error of prediction only slightly increased (R² = 0.53, RMSEP = 0.10, n = 34). This model based on Rock-Eval 6 thermal analysis can thus be used to predict the proportion of centennially persistent SOC with known uncertainty in new soil samples from different pedoclimates, at least for sites that have similar Rock-Eval 6 thermal characteristics to those included in the calibration set. Our study reinforces the evidence that there is a link between the thermal and biogeochemical stability of soil organic matter and demonstrates that Rock-Eval 6 thermal analysis can be used to quantify the size of the centennially persistent organic carbon pool in temperate soils.