Carbon Sequestration in Agricultural Soils: A Multidisciplinary Approach to Innovative Methods
In particular, there is good agreement for European temperate regions, as the default parameterization of RothC used here was obtained for those conditions originally for measurements of SOC in Harpenden, United Kingdom [ 30 ]. However, this is not the case for all regions of the World.
Despite the notable exceptions mentioned earlier, the estimation agreement is lower in regions with significant different climate conditions to those where RothC were calibrated. Regarding the mineralization rate, an average range reported for sorghum production in Burkina Faso [ 79 ] is 1. The differences can be justified due the differences in climatic variables between studies e. The average rate for fertilized grasslands in England [ 80 ] are 3. For the same region we obtained 0.
Carbon Sequestration in Agricultural Soils
A systematic comparison of our results with individual studies would require a large meta-analysis of SOC measurement studies, which is outside the scope of this study. Our results are valid over relatively aggregated regions that are approximately homogeneous i. UHTUs , but the studies in the literature are valid for specific points within those regions. It would be necessary to aggregate the results of those studies into broader areas in order to compare them with our regionally applicable results.
At the moment, these comparisons provide promising indications that our results depict the main trends when comparing regions and LU classes. This provides some qualitative assurance for the main insights obtained from the results in this paper. In most cases where the literature reports measured SOC stocks that are significantly different from our results, the relative comparisons between LU classes and locations are very rarely different. Besides individual studies, we also compared our results with pre-established SOC databases.
This enables a systematic assessment of the agreement between results. This fact can be explained by the combination of three factors: temperatures are relatively low temperate, precipitation is relatively moderate in the case of British Isles or low Southeast France and organic soils, which are typically high in SOM [ 17 , 84 , 85 ], are present. For sugar beet and rapeseed no spatial correlation was found. The fact that a positive correlation was found is reassuring, as a stronger correlation could not be expected. Most model applications use only defaults i.
The HUM pool is most often re-parameterized as for example [ 86 — 88 ] as it has the most sensitive kinetic constant due to the longer residence time.
Regarding land classes data shown in S2 File , all three main LU types cropland, grassland and forest are covered by the sampled papers, and for each of them multiple sub-classes are considered—e. Forest is the class that required more re-parametrizations 23 out of 52 applications , mainly in the HUM decomposition rate. Nevertheless, twenty-two of those re-parametrizations are from only one study Shirato et al. Croplands required the least changes only 5 , which was expected considering that RothC was originally developed for croplands.
This low number of re-parameterizations in studies that applied the model at very fine scales provides some assurance that the default configuration of the model used in this work works well for most regions of the World. The resilience of our results is also demonstrated when comparing our results to results obtained using different parametrizations for specific regions.
For example, RothC was originally developed for humid regions and is known to overestimate C mineralization in dry regions [ 89 ]. A small change in the soil moisture rate modifier was proposed where the minimum value of the rate modifier was changed to 0. The average obtained in each model application for each land class always falls within the confidence interval obtained in the other. Therefore, despite of the reported overestimation of C mineralization under dry conditions, there is no statistically significant difference in results for our application at UHTU scale.
As in this paper, all of them used the model without re-parametrization. Those studies include the main three LU classes, cropland, grassland and forest, without discriminating between sub-classes e.
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For grasslands, the model application performed by Smith et al. Our study is therefore aligned with past practices in terms of parameter selection but adds significant regional and LU detail when compared to those prior applications. Both papers focus on the effects of climate change and estimate a SOC loss in cropland and grassland areas in Europe due to warmer temperatures. They also note that SOC loss may be slowed by decreased soil moisture. Those results cannot be directly compared to the results obtained in this paper because our study was not forward-looking and did not take the effects of future climate change temperature and precipitation into account.
However, our results have implications for studies aimed to estimating the effect of climate change on SOC loss. In the same UHTU, land occupation with one crop type could lead to SOC gains and with another crop to SOC loss due to different yields and their response to changing climate conditions. These results therefore suggest that future similar studies should consider at minimum the same level of differentiation between crop types and regions used in this paper. The use of a Monte Carlo approach in this work also had additional value as it avoided assuming that the number chosen for each input variable was representative of each entire region.
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Input data varied per iteration according to a pre-set probability distribution depicting the regional variability of the data within each UHTU. The average of the iterations performed for each UHTU and land class, which is indicated in this work as the most likely result with accompanying uncertainty intervals , is therefore representative of the UHTUs they depict.
We selected RothC for this paper, but RothC is one of several soil process-based models available that could have been used instead. Originally, all models simulate the first 20 cm depth, but all were already used at 30 cm depth. All models run using a monthly time step. Some variables are difficult to obtain for a global application with the characteristics of the one in this paper.
We focused specifically on processes influencing C dynamics, and, as RothC includes only the C cycle, the number of parameters involved and data required is lower. The lower number of required input data makes a spatially differentiated and crop-specific global assessment feasible. The simplicity of RothC, however, comes at a cost as it does not model some processes. It is the only of the three models that omits leaching of organic matter and that does not include a management practices module other models can simulate the effects of fertilization, harvesting, fire, grazing, irrigation and erosion—in some cases through extensions or more recent modules built on top of the original model.
The lack of depictions of these processes are limitations that may be overcome through extensions to RothC and are suggested for further revisions of work similar to what we present in this paper. Despite several limitations inherent to the task of performing global SOC modelling, such as the simplified depiction of soil biogeochemical processes by the model and uncertainty of the input data e.
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Our results are also robust to differences within each LU class, e. It is likely that general estimation accuracy could be improved using local parametrization of the model, as performed by some prior RothC applications [ 86 — 88 ], for those regions and classes where the estimated results deviate from field-measured values. We expect locally specific parameterizations to improve results because the agreement of the model with other published studies seems to be better for temperate regions closer to the ones used for the original calibration of the model. The quantitative comparison of results with data presented here is also promising despite the fact that more work is needed to assess the regions and classes for which the model works properly and the ones where the model fails to depict realistic SOC stock dynamics.
Attainable SOC stock and mineralization rates are also influenced by other methodological choices done in this work. Only input variables had an assigned probability distribution within each UHTU, but even for those we assumed as a simplification that all input variables were normally distributed. When we obtained the data from a geospatial dataset i. For some data, however, we were unable to do so as we had to assume a distribution based on one likely value plus a confidence interval e.
IPCC-based data on yields. It is unlikely that all input variables are in fact distributed normally. For example, clay content is frequently used in modelling according to a normal distribution e. This information influences the depiction of intra-regional variability through the uncertainty quantified using Monte Carlo analysis. Soil C input is a critical factor in explaining differences between attainable SOC, as demonstrated by complementary analysis reported in S1 File. In this work, for all agricultural LU classes, we considered constant plant yields, as well as constant aboveground and belowground residue C inputs and C from manure application.
This method introduces error in cases where belowground plant growth does not scale with aboveground yield. There is some evidence that belowground C varies less between crops than aboveground C. For example, Cagnarini et al. Taghizadeh-Toosi et al. In general, the literature overall shows that the type of crop influences belowground biomass e. The method used in this paper prevented us from simulating intra-annual changes in crop residue C inputs from belowground as we use constant near-past average monthly temperature and precipitation.
Studies that, unlike this one, are interested in understanding the dynamics of SOC gains and losses due to climate change or other dynamic processes should take into account that belowground biomass can be nearly uncorrelated with the response of the aboveground biomass, which is typically easier to estimate. Additionally, in order to simplify the calculation of C inputs, we also considered the same C content of the crop, forest and grassland residues i. While there is good evidence for the C content of living plant biomass [ 19 ], there are many types of manure with varying chemical compositions [ 96 ].