Abstract Modelling N transformations within cropping systems is crucial for N management optimization in order to increase N use efficiency and reduce N losses. Such modelling remains challenging because of the complexity of N cycling in soil–plant systems. In the current study, the uncertainties of six widely used process-based models (PBMs), including APSIM, CROPSYST, DAISY, FASSET, HERMES and STICS, were tested in simulating different N managements (catch crops (CC) and different N fertilizer rates) in 12-year rotations in Western Europe. Winter wheat, sugar beet and pea were the main crops, and radish was the main CC in the tested systems. Our results showed that PBMs simulated yield, aboveground biomass, N export and N uptake well with low RMSE values, except for sugar beet, which was generally less well parameterized. Moreover, PBMs provided more accurate crop simulations (i.e. N export and N uptake) compared to simulations of soil (N mineralization and soil mineral N (SMN)) and environmental variables (N leaching). The use of multi-model ensemble mean or median of four PBMs significantly reduced the mean absolute percentage error (MAPE) between simulations and observations to less than 15% for yield, aboveground biomass, N export and N uptake. Multi-model ensemble also significantly reduced the MAPE for net N mineralization and annual N leaching to around 15%, while it was larger than 20% for SMN. Generally, PBMs well simulated the CC effects on N fluxes, i.e. increasing N mineralization and reducing N leaching in both short-term and long-term, and all PBMs correctly predicted the effects of the reduced N rate on all measured variables in the study. The uncertainties of multi-model ensemble for N mineralization, SMN and N leaching were larger, mainly because these variables are influenced by plant-soil interactions and subject to cumulative long-term effects in crop rotations, which makes them more difficult to simulate. Large differences existed between individual PBMs due to the differences in formalisms for describing N processes in soil–plant systems, the skills of modelers and the model calibration level. In addition, the model performance also depended on the simulated variables, for instance, HERMES and FASSET performed better for yield and crop biomass, APSIM, DAISY and STICS performed better for N export and N uptake, STICS provided best simulation for SMN and N leaching among the six individual PBMs in the study, but all PBMs met difficulties to well predict either average or variance of soil N mineralization. Our results showed that better calibration for soil N variables is needed to improve model predictions of N cycling in order to optimize N management in crop rotations.

中文翻译：

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使用基于过程的模型模拟欧洲作物轮作中 N 吸收、净 N 矿化、土壤矿物质 N 和 N 浸出的不确定性

摘要 在种植系统中模拟 N 转换对于 N 管理优化至关重要，以提高 N 使用效率并减少 N 损失。由于土壤-植物系统中氮循环的复杂性，这种建模仍然具有挑战性。在当前的研究中，六种广泛使用的基于过程的模型 (PBM) 的不确定性，包括 APSIM、CROPSYST、DAISY、FASSET、HERMES 和 STICS，在模拟不同的氮管理（捕捞作物 (CC) 和不同的氮肥施用量）中进行了测试) 在西欧 12 年轮换。冬小麦、甜菜和豌豆是主要作物，萝卜是测试系统中的主要CC。我们的结果表明，除了甜菜之外，PBM 模拟了产量、地上生物量、氮输出和氮吸收，RMSE 值较低，但通常参数化较差。而且，与土壤模拟（氮矿化和土壤矿物质氮 (SMN)）和环境变量（氮浸出）相比，PBM 提供了更准确的作物模拟（即氮输出和氮吸收）。使用四个 PBM 的多模型集合平均值或中位数，将模拟和观察之间的平均绝对百分比误差 (MAPE) 显着降低到产量、地上生物量、N 输出和 N 吸收的 15% 以下。多模型集合还显着降低了净 N 矿化和年 N 浸出的 MAPE 至 15% 左右，而 SMN 则大于 20%。一般而言，PBM 很好地模拟了 CC 对 N 通量的影响，即在短期和长期内增加 N 矿化和减少 N 浸出，并且所有 PBM 都正确预测了降低 N 速率对研究中所有测量变量的影响。N 矿化、SMN 和 N 浸出的多模型集合的不确定性较大，主要是因为这些变量受植物 - 土壤相互作用的影响，并受到作物轮作中累积的长期影响，这使得它们更难以模拟。由于描述土壤-植物系统中 N 过程的形式主义、建模者的技能和模型校准水平的不同，单个 PBM 之间存在很大差异。此外，模型性能还取决于模拟变量，例如 HERMES 和 FASSET 在产量和作物生物量方面表现更好，APSIM、DAISY 和 STICS 在 N 输出和 N 吸收方面表现更好，STICS 为 SMN 和 N 浸出提供了最佳模拟在研究中的六个单独的 PBM 中，但所有 PBM 都难以很好地预测土壤 N 矿化的平均值或方差。我们的结果表明，需要更好地校准土壤 N 变量以改进 N 循环的模型预测，以优化作物轮作中的 N 管理。