Hot regions of labile and stable soil organic carbon in Germany – Spatial variability and driving factors

Abstract

Abstract. Atmospheric carbon dioxide levels can be mitigated by sequestering carbon in the soil. Sequestration can be facilitated by agricultural management, but its influence is not the same on all soil carbon pools, as labile pools with a high turnover may be accumulated much faster but are also more vulnerable to losses. The aims of this study were to (1) assess how soil organic carbon (SOC) is distributed among SOC fractions on a national scale in Germany, (2) identify factors influencing this distribution and (3) identify regions with high vulnerability to SOC losses. The SOC content and proportion of two different SOC fractions were estimated for more than 2500 mineral topsoils (< 87 g kg−1 SOC) covering Germany, using near-infrared reflectance spectroscopy. Drivers of the spatial variability in SOC fractions were determined using the machine learning algorithm cforest. The SOC content and proportions of fractions were predicted with good accuracy (SOC content: R2 = 0.87–0.90; SOC proportions: R2 = 0.83; ratio of performance to deviation (RPD): 2.4–3.2). The main explanatory variables for the distribution of SOC among the fractions were soil texture, bulk soil C ∕ N ratio, total SOC content and pH. For some regions, the drivers were linked to the land-use history of the sites. Arable topsoils in central and southern Germany were found to contain the highest proportions and contents of stable SOC fractions, and therefore have the lowest vulnerability to SOC losses. North-western Germany contains an area of sandy soils with unusually high SOC contents and high proportions of light SOC fractions, which are commonly regarded as representing a labile carbon pool. This is true for the former peat soils in this area, which have already lost and are at high risk of losing high proportions of their SOC stocks. Those “black sands” can, however, also contain high amounts of stable SOC due to former heathland vegetation and need to be treated and discussed separately from non-black sand agricultural soils. Overall, it was estimated that, in large areas all over Germany, over 30 % of SOC is stored in easily mineralisable forms. Thus, SOC-conserving management of arable soils in these regions is of great importance.

Combined Size and Density Fractionation of Soils for Investigations of Organo-Mineral Interactions

“…SOC recovery was generally within the range of reports from other studies 53,54 and remarkably constant considering the large variation in initial SOC content ( Table 5). Most samples showed a SOC recovery rate of 80-85%.…”

“…There are, however, indications that the organic matter present in macroaggregates may be more similar to the fLF stricto sensu than to the oLF released by high-energy sonication 18 . Some authors have even proposed a low-energy sonication step to isolate the pool of free and weakly mineral-interacting organic matter, termed 'intra-aggregate particulate organic matter', iPOM 54 .…”

Stocks of organic carbon in German agricultural soils—Key results of the first comprehensive inventory

“…2) and are therefore also referred to as ‘Black Sands' ( Vos et al, 2018). Also in parts of Denmark, Belgium and The Netherlands, these ‘Black Sands' are an acknowledged phenomenon and are mostly linked to a specific pedogenesis (podzolization) or land cover history as heathland or peatland ( Thomsen et al, 2008; Sleutel et al, 2008, 2010; Vos et al, 2018). These two factors are probably interlinked, with land use influencing podzolization and vice versa .…”

“…Calluna vulgaris , a major heathland plant species, is acknowledged to form specifically resistant plant litter, with high amounts of polyphenolic substances ( Sleutel et al, 2008). Indeed, Vos et al (2018) found that SOC in those soils consisted of up to 85% particulate organic matter. However, some of the northern German SOC‐rich sands are former peatlands, but not complying with our definition of organic soils anymore, or were formed by plaggen manuring ( Vos et al, 2018).…”

“…Several case studies using the inventory dataset have also been published. For example, Vos et al (2019) used a machine learning algorithm to explain the country‐scale variability in SOC stocks with a wide range of pedoclimatic and management variables, while Vos et al (2018) used a combination of near infrared spectroscopy and SOC fractionation at selected sites to identify hot regions of labile SOC. The degree of severe soil compaction and the potential for alleviating root‐restricting layers in German agricultural soils have also been investigated ( Schneider and Don , 2019a; 2019b).…”