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 southwestern Germany at 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 obtained global and the different local clusters of the dataset were further analyzed for the relationships between SOC and mineral phase parameters in order to assess specific parameter combinations explaining SOC and its labile fractions. 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 global dataset, we found significant correlations between SOC and its labile fractions hot water-extractable C (HWEC) and microbial biomass C (MBC), respectively and the predictors, yet correlation coefficients were partially low. 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 models-predicted SOC values revealed acceptable to very good regression coefficients (R² = 0.41–0.91). Thereby, the predictors and predictor combinations clearly differed between models obtained for the whole data set and the different cluster groups. At a local scale site specific combinations of parameters explained the variability of organic matter notably better, while the application of global models to local clusters resulted in less sufficient performance. Independent from that, the overall explained variance generally decreased in the order SOC > HWEC > MBC, showing that labile fractions depend less on soil properties than on organic matter input and turnover in soil.

中文翻译：

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土壤有机质和不稳定部分取决于特定的局部参数组合

摘要。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局部范围内因母材、土壤质地类别和土地利用而异。为此，从德国西南部的四个地区的耕地和草地上采样了表土，这些地区具有不同的土壤母质。主成分分析 (PCA) 根据母体材料和土壤质地揭示了不同的数据聚类，这些数据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。进一步分析获得的数据集的全局和不同局部集群的 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素（例如细粉砂加上粘土和土壤氧化物）。关于全球数据集，我们分别发现 SOC 与其不稳定组分热水可提取 C (HWEC) 和微生物生物量 C (MBC) 以及预测因子之间存在显着相关性，但相关系数部分偏低。混合效应模型用于确定特定参数组合，这些组合可以显着解释 SOC 及其不同簇的不稳定部分。比较测量和混合效应模型预测的 SOC 值显示可接受的非常好的回归系数 (R² = 0.41–0.91)。因此，预测变量和预测变量组合在为整个数据集和不同聚类组获得的模型之间明显不同。在局部尺度上，特定的参数组合可以更好地解释有机物质的可变性，而将全局模型应用于局部集群导致性能不够充分。与此无关，总体解释方差通常按照 SOC > HWEC > MBC 的顺序降低，