Abstract Crop nitrogen (N) uptake is a key process in soil-crop models. This process affects crop growth and soil N cycling and determines crop quality. However, crop N uptake modeling remains uncertain because of the various N dilution curve approaches adopted in soil-crop models. In this study, four different representative N dilution curve approaches (i.e., DAISY, M1; CERES and RZWQM, M2; EPIC, M3; and CROPSYST or STICS, M4) were incorporated into a soil-crop model platform (WHCNS), and their effects on crop N uptake and crop growth simulation under different water and N stresses were evaluated via global sensitivity analysis methods. The three-year field experiment data of winter wheat-summer maize rotation under different water and N management practices were used to test the model. Results showed that the WHCNS model performed well in modeling the supplies of soil water and mineral N. The values of statistical indices for crop N uptake, LAI, dry matter and yield simulation by the four methods were within the acceptable ranges, and had the relative mean square error (RRMSE) 0.81 and Nash and Sutcliffe index (NSE) > 0.37. However, the M2 method performed well using the minimum input parameters, and hence recommended to simulate crop N uptake in soil-crop models. In this study, the dataset of high water and N input treatment was more suitable for model parameter estimation to reduce uncertainty, and the datasets of middle and low water and N input treatments was appropriate to validate the model. These information were useful to guide selecting the modeling method and the model calibration dataset.

中文翻译：

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不同氮稀释曲线方法对作物氮吸收动态模拟的全局敏感性和不确定性分析

摘要 作物氮 (N) 吸收是土壤作物模型中的关键过程。这个过程影响作物生长和土壤氮循环并决定作物质量。然而，由于土壤 - 作物模型中采用了各种 N 稀释曲线方法，作物 N 吸收模型仍然不确定。在本研究中，四种不同的代表性氮稀释曲线方法（即 DAISY，M1；CERES 和 RZWQM，M2；EPIC，M3；和 CROPSYST 或 STICS，M4）被纳入土壤作物模型平台（WHCNS），以及它们的通过全局敏感性分析方法评估了在不同水和氮胁迫下对作物吸氮和作物生长模拟的影响。利用不同水氮管理措施下冬小麦-夏玉米轮作的三年田间试验数据对模型进行检验。结果表明，WHCNS模型在模拟土壤水分和矿质氮供给方面表现良好。4种方法对作物吸氮量、LAI、干物质和产量模拟的统计指标值均在可接受范围内，并具有相对均方误差 (RRMSE) 0.81 和纳什和萨特克利夫指数 (NSE) > 0.37。然而，M2 方法在使用最小输入参数时表现良好，因此建议在土壤 - 作物模型中模拟作物 N 吸收。本研究中高水N输入处理数据集更适合模型参数估计以减少不确定性，中低水N输入处理数据集适合模型验证。这些信息有助于指导选择建模方法和模型校准数据集。