Background: There is considerable uncertainty about the actual size of the global soil organic carbon (SOC) pool and its spatial distribution due to insufficient and heterogeneous data coverage.Aims: We aimed to assess the size of the German agricultural SOC stock and develop a stratification approach that could be used in national greenhouse gas reporting.Methods: Soils from a total of 3104 sites, comprising 2234 croplands, 820 permanent grasslands and 50 sites with permanent crops (vineyards, orchards) were sampled in a grid of 8 × 8 km to a depth of 100 cm in fixed depth increments. In addition, a decade of management data was recorded in a questionnaire completed by farmers. Two different approaches were used to stratify cropland and grassland mineral soils and derive homogeneous groups: stratification via soil type (pedogenesis) and via SOC‐relevant soil properties.Results: A total of 146 soils were identified as organic soils, which stored by far the highest average SOC stock of 528 ± 201 Mg ha−1 in 0–100 cm depth. Of the mineral soils, croplands and permanent crops stored on average 61 ± 25 and 62 ± 25 Mg ha−1 in 0–30 cm (topsoil) and 35 ± 30 and 44 ± 28 Mg ha−1 in 30–100 cm (subsoil), while permanent grasslands stored significantly more SOC (88 ± 32 and 47 ± 50 Mg ha−1 in topsoil and subsoil). Overall, topsoils stored 67 ± 14% and subsoils 33 ± 14% of total SOC stocks. Soil C:N ratio, clay content and groundwater level were major factors that explained the spatial variability of SOC stocks in mineral soils. Accordingly, Podzols, Gleysols and Vertisols were found to have the highest SOC stocks.Conclusions: Stratification via soil properties yielded the most comparable cropland and grassland strata and is thus preferable for estimating land‐use change effects, e.g., for greenhouse gas inventories.In total, 2.5 Pg C are stored in the upper 100 cm of German agricultural soils, making them the largest organic carbon pool in terrestrial ecosystems of Germany. This bares a large responsibility for the agricultural sector and society as a whole to maintain and, if possible, enhance this pool.
Abstract. Partitioning soil organic carbon (SOC) into two kinetically different fractions that are stable or active on a century scale is key for an improved monitoring of soil health and for more accurate models of the carbon cycle. However, all existing SOC fractionation methods isolate SOC fractions that are mixtures of centennially stable and active SOC. If the stable SOC fraction cannot be isolated, it has specific chemical and thermal characteristics that are quickly (ca. 1 h per sample) measurable using Rock-Eval® thermal analysis. An alternative would thus be to (1) train a machine-learning model on the Rock-Eval® thermal analysis data for soil samples from long-term experiments for which the size of the centennially stable and active SOC fractions can be estimated and (2) apply this model to the Rock-Eval® data for unknown soils to partition SOC into its centennially stable and active fractions. Here, we significantly extend the validity range of a previously published machine-learning model (Cécillon et al., 2018) that is built upon this strategy. The second version of this model, which we propose to name PARTYSOC, uses six European long-term agricultural sites including a bare fallow treatment and one South American vegetation change (C4 to C3 plants) site as reference sites. The European version of the model (PARTYSOCv2.0EU) predicts the proportion of the centennially stable SOC fraction with a root mean square error of 0.15 (relative root mean square error of 0.27) at six independent validation sites. More specifically, our results show that PARTYSOCv2.0EU reliably partitions SOC kinetic fractions at its northwestern European validation sites on Cambisols and Luvisols, which are the two dominant soil groups in this region. We plan future developments of the PARTYSOC global model using additional reference soils developed under diverse pedoclimates and ecosystems to further expand its domain of application while reducing its prediction error.
Numerous approaches have been developed to isolate fast and slow cycling soil organic carbon (SOC) pools using physical and chemical fractionation. Most of these methods are complex, expensive, and time consuming and unsuited for high-throughput application, such as for regional scale assessments. For simpler and faster fractionation via particle size the key issue is the dispersion of soil. It is unclear how the initial dispersion of soil affects the turnover rates of isolated fractions. We investigated five commonly used dispersion methods using different intensities: shaking in water, shaking in water with glass beads, ultrasonication at 100 and 450 J ml−1 and sodium hexametaphosphate (Na-HMP). We used soils from long-term field experiments that included a change from C3 to C4 vegetation and adjacent control sites using δ13C isotope ratio mass spectrometry. We evaluated the degree of C3/C4 moieties of the fractions, mass and carbon recovery and reproducibility as well as the time expenditures of the dispersions, sieving and drying techniques to develop an efficient and cheap fractionation method. Our results indicate that ultrasonication as well as H2O treatment with and without glass beads resulted in fractions with different turnover. Moreover, isolation performances depended on soil texture. While the isolation of the fractions using water with and without glass beads was equivalent to ultrasonication in soils with low clay contents, these methods had limited potential for soils with high clay contents. Furthermore, treatment with water alone had less reproducible results than other tested methods. The SOC recovery was comparable and satisfactory amongst non-chemical dispersion methods and reached over 95% for each of these methods. The use of Na-HMP was unsuccessful due to high time expenditures and strong SOC leaching. We propose particle size fractionation combined with ultrasonic dispersion as a fast and highly reliable method to quantify slow and fast cycling SOC pools for a wide range of soil types and textures from agricultural sites in central Europe.
Soil organic carbon (SOC) of agricultural soils is observed to decline in many parts of the world. For deconfounding management and climate change effects, the latter needs to be estimated comprehensively. In this study, an established FAO framework was used to model global agricultural topsoil SOC stock dynamics from 1919 to 2018 as attributable to climate change. On average, global agricultural topsoils lost 2.5 ± 2.3 Mg C ha− 1 with constant net primary production (NPP) or 1.6 ± 3.4 Mg C ha− 1 when NPP was modified by temperature and precipitation. Regional variability could be explained by the complex patterns of changes in temperature and moisture, as well as initial SOC stocks. However, average SOC losses have been an intrinsic and persistent feature of climate change in all climatic zones. This needs to be taken into consideration in reporting or accounting frameworks and halted in order to mitigate climate change and secure soil health.
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