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.

中文翻译：

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土壤有机碳和不稳定组分的含量取决于矿物相特征的局部组合

土壤有机质（SOM）是陆地生态系统不可或缺的组成部分。土壤有机碳 (SOC) 动态受到许多众所周知的非生物因素的影响，例如粘土含量、土壤 pH 值或土壤氧化物。这些参数相互影响，并根据当地条件对 SOC 的影响有所不同。为了研究后者，对 SOC 积累对参数和参数组合的依赖性进行了统计评估，这些参数和参数组合在当地尺度上有所不同，具体取决于母材、土壤质地类别和土地利用。为此，我们从德国西南部四个地区不同土壤母质的耕地和草地上采集了表土。主成分分析 (PCA) 揭示了根据母质和土壤质地的明显数据聚类，在当地采样区域之间差异很大，而土地利用仅在很小程度上解释了 PCA 结果。PCA 集群被区分为包含整个数据集或其主要部分的总集群和仅代表数据集较小部分的本地集群。分析所有簇的 SOC 浓度 (SOC%) 和矿物相参数之间的关系，以评估解释 SOC 及其不稳定部分热水可萃取 C (HWEC) 和微生物生物量 C (MBC) 的特定参数组合。分析的重点是土壤参数，这些参数被认为是 SOC 发生和稳定的可能预测因子（例如细淤泥加粘土和土壤氧化物）。关于总集群，我们通过双变量模型发现 SOC、其不稳定部分 HWEC 和 MBC 以及应用的预测变量之间存在显着关系。然而，部分低解释方差表明双变量模型的适用性有限。因此，混合效应模型被用来识别特定的参数组合，这些参数组合显着解释了不同集群的 SOC 及其不稳定部分。比较测量的和混合效应模型预测的 SOC 值显示可以接受非常好的回归系数（混合效应模型用于识别特定参数组合，这些参数组合显着解释了不同簇的 SOC 及其不稳定部分。比较测量的和混合效应模型预测的 SOC 值显示可以接受非常好的回归系数（混合效应模型用于识别特定参数组合，这些参数组合显着解释了不同簇的 SOC 及其不稳定部分。比较测量的和混合效应模型预测的 SOC 值显示可以接受非常好的回归系数（R ² =0.41 –0.91) 和低到可接受的均方根误差 (RMSE  =  0.20 %–0.42 %)。因此，为整个数据集和不同集群组获得的模型之间的预测变量和预测变量组合明显不同。在局部尺度上，特定地点的参数组合明显更好地解释了有机碳的变异性，而将总模型应用于局部集群导致更少的解释方差和更高的 RMSE。与此无关，由边际固定效应解释的方差按 SOC  >  HWEC  >  MBC 的顺序降低，表明不稳定部分对土壤特性的依赖较少，但可能更多地依赖于有机碳输入和土壤周转等过程。