{'fraction_non_alphanumeric': 0.02680692229385816, 'fraction_numerical': 0.004411265693926026, 'mean_word_length': 5.32618025751073, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 2, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Content of soil organic carbon and labile fractions depend on local combinations of mineral-phase characteristics

Abstract. Soil organic matter (SOM) is an indispensable component of
terrestrial ecosystems. Soil organic carbon (SOC) dynamics are influenced by
a number of well-known abiotic factors such as clay content, soil pH, or pedogenic oxides. These parameters interact with each other and vary in
their influence on SOC depending on local conditions. To investigate the
latter, the dependence of SOC accumulation on parameters and parameter
combinations was statistically assessed that vary on a local scale depending
on parent material, soil texture class, and land use. To this end, topsoils were sampled from arable and grassland sites in south-western Germany in four regions with different soil parent material. Principal component analysis
(PCA) revealed a distinct clustering of data according to parent material
and soil texture that varied largely between the local sampling regions,
while land use explained PCA results only to a small extent. The PCA
clusters were differentiated into total clusters that contain the entire
dataset or major proportions of it and local clusters representing only a
smaller part of the dataset. All clusters were analysed for the relationships between SOC concentrations (SOC %) and mineral-phase parameters in order to assess specific parameter combinations explaining SOC
and its labile fractions hot water-extractable C (HWEC) and microbial biomass C (MBC). Analyses were focused on soil parameters that are known as possible predictors for the occurrence and stabilization of SOC (e.g. fine
silt plus clay and pedogenic oxides). Regarding the total clusters, we found
significant relationships, by bivariate models, between SOC, its labile
fractions HWEC and MBC, and the applied predictors. However, partly low explained variances indicated the limited suitability of bivariate models. Hence, mixed-effect models were used to identify specific parameter combinations that significantly explain SOC and its labile fractions of the different
clusters. Comparing measured and mixed-effect-model-predicted SOC values revealed acceptable to very good regression coefficients (R2=0.41–0.91)
and low to acceptable root mean square error (RMSE = 0.20 %–0.42 %).
Thereby, the predictors and predictor combinations clearly differed between
models obtained for the whole dataset and the different cluster groups. At a local scale, site-specific combinations of parameters explained the variability of organic carbon notably better, while the application of total
models to local clusters resulted in less explained variance and a higher
RMSE. Independently of that, the explained variance by marginal fixed effects decreased in the order SOC > HWEC > MBC,
showing that labile fractions depend less on soil properties but presumably
more on processes such as organic carbon input and turnover in soil.
