Factors controlling the variation in organic carbon stocks in agricultural soils of Germany

Abstract

This study gives an overview of soil organic carbon (SOC) stocks in Germany's agricultural soils, and quantifies and explains the influence of explanatory variables such as land use and management, soil type and climate. Over 2500 agricultural sites were sampled and their SOC stocks determined, together with other soil properties. Machine‐learning algorithms were used to identify the most important variables. Land use, land‐use history, clay content and electrical conductivity were the main predictors in the topsoil, whereas bedrock material, relief and electrical conductivity governed the variation in subsoil carbon stocks. We found that 32% of all soil profiles were anthropogenically transformed. The influence of climate variables was surprisingly small, whereas site variables, in particular in the subsoil, explained a large proportion of the variation in soil carbon. The understanding of SOC dynamics at the regional scale requires a thorough description of the spatial variation in soil properties. The effect of agronomic management on SOC stocks was important near the soil surface, but was mainly attributable to land use (grassland or arable and not to other management factors. Highlights Factors affecting spatial variation of soil organic carbon stocks are largely unknown. More than 200 possible predictors for this variation were assessed for over 2500 sites. Land use (history), texture, electrical conductivity, bedrock and relief were main controlling factors of carbon stock variation. Aggregation and carbon storage capacity of soil determine much of the spatial variation in carbon stocks.

Origin of carbon in agricultural soil profiles deduced from depth gradients of C:N ratios, carbon fractions, δ13C and δ15N values

“…Using this method, for example, Hobley et al 2015found climate variables to be key for explaining soil organic C storage in Eastern Australia. Also, in southeast Germany, Wiesmeier et al (2012) identified precipitation and temperature to be important for explaining soil organic C. However, for whole Germany, our study and Vos et al (2019) found climate to be insignificant for explaining soil organic C. Such seemingly contrasting findings can be confusing -they illustrate three major shortcomings of Random Forests in explaining soil organic C: & In contrast to mechanistic models, Random Forest models can only describe patterns within the predictor space they were trained on (Meyer & Pebesma 2020). Random Forest models that are trained to predict organic C in mineral soil (as in this study) will fail to predict organic C in organic soil because, per definition, the organic C content in organic soil is much higher than in mineral soil.…”

“…Sorption of organic C on clay surfaces decreases C turnover (von Luetzow et al 2008), whereas microbes allocated on mineral surfaces directly incorporate metabolised C into their biomass and necromass (Kögel-Knabner and Amelung 2014). This makes soil texture a key variable for explaining the C stocks of agricultural soils in Germany (Vos et al 2019).…”

Sediment and soil organic carbon loss during continuous extreme scouring events on the Loess Plateau

“…Consistent with previous studies, we concluded that organic C is mainly combined with silt and clay in sediment. We found a positive correlation between organic C and clay and a negative relationship between organic C and sand (Vos et al., 2019). Previous studies have found that organic matter can be combined with finer soil particles into an organic–inorganic complex, or agglomerate, which increases the chemical binding capacity and chemical stability of the organic material, reduces the mineralization loss of the organic material, and enriches the organic material of soil particles (Koiter et al., 2017; Wang et al., 2019; Zhen, Ping, Ming, & Jun, 2005).…”

Soil Organic Carbon Mapping Using LUCAS Topsoil Database and Sentinel-2 Data: An Approach to Reduce Soil Moisture and Crop Residue Effects

“…The ratio of performance to deviation (RPD), i.e., the ratio between the standard deviation and the root mean square error (RMSE), was used as a measure for the estimation accuracy. Additionally, 78 samples of the GASI+Demmin dataset were collected in the framework of the German Agricultural Soil Inventory [21]. These samples were collected from a soil profile in cropland sites at 0-10 cm depth using three 200 cm 3 soil cores.…”