The potential contributions of exogenous organic matters (EOMs) to soil organic C and mineral N supply depend on their C and N mineralization, which can be assessed in laboratory incubations. Such incubations are essential to calibrate decomposition models, because not all EOMs can be tested in the field. However, EOM incubations are resource-intensive. Therefore, easily measurable EOM characteristics that can be useful to predict EOM behaviour are needed. We quantified C and N mineralization during the incubation of 663 EOMs from five groups (animal manures, composts, sewage sludges, digestates and others). This represents one of the largest and diversified set of EOM incubations. The C and N mineralization varied widely between and within EOM subgroups. We simulated C and N mineralization with a simple generic decomposition model. Three calibration methods were compared. Individual EOM calibration of the model yielded good model performances, while the use of a unique parameter set per EOM subgroup decreased the model performance, and the use of two EOM characteristics to estimate model parameters gave an intermediate model performance (average RMSE-C values of 32, 99 and 65 mg C g⁻¹ added C and average RMSE-N values of 50, 126 and 110 mg N g⁻¹ added N, respectively). Because of the EOM variability, individual EOM calibration based on incubation remains the recommended method for predicting most accurately the C and N mineralization of EOMs. However, the two alternative calibration methods are sufficient for the simulation of EOMs without incubation data to obtain reasonable model performances.

中文翻译：

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量化和模拟来自不同外源有机物的碳和氮矿化

外源有机物 (EOM) 对土壤有机碳和矿物氮供应的潜在贡献取决于它们的碳和氮矿化，这可以在实验室孵化中进行评估。这种孵化对于校准分解模型至关重要，因为并非所有 EOM 都可以在现场进行测试。然而，EOM 孵化是资源密集型的。因此，需要可用于预测 EOM 行为的易于测量的 EOM 特征。我们量化了来自五组（动物粪便、堆肥、污水污泥、消化物等）的 663 个 EOM 的孵化过程中的 C 和 N 矿化。这代表了最大和多样化的 EOM 孵化集之一。C 和 N 矿化在 EOM 亚组之间和内部变化很大。我们用一个简单的通用分解模型模拟了 C 和 N 矿化。比较了三种校准方法。模型的单独 EOM 校准产生了良好的模型性能，而使用每个 EOM 子组的唯一参数集降低了模型性能，并且使用两个 EOM 特征来估计模型参数给出了中间模型性能（平均 RMSE-C 值为32、99 和 65 毫克 Cg^-1添加 C 和平均 RMSE-N 值分别为 50、126 和 110 mg N g ^-1添加 N）。由于 EOM 的可变性，基于孵化的单独 EOM 校准仍然是最准确地预测 EOM 的 C 和 N 矿化的推荐方法。然而，这两种替代校准方法足以模拟没有孵化数据的 EOM，以获得合理的模型性能。