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首页> 外文期刊>European Journal of Agronomy >Global sensitivity and uncertainty analysis of the dynamic simulation of crop N uptake by using various N dilution curve approaches
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Global sensitivity and uncertainty analysis of the dynamic simulation of crop N uptake by using various N dilution curve approaches

机译:通过使用各种N稀释曲线方法,全局敏感性和不确定分析作物N采样的动态模拟

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摘要

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) 24 %, index of agreement (IA) > 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.
机译:作物氮(n)摄取是土壤作物模型的关键过程。该过程影响作物生长和土壤循环并确定作物质量。然而,由于土壤作物模型中采用的各种N稀释曲线方法,作物N摄取模型仍然不确定。在这项研究中,四种不同的代表性N稀释曲线方法(即菊花,M1; CERES和RZWQM,M2; EPIC,M3;以及经纪人,M4,M4)被纳入土壤 - 作物模型平台(WHCN),及其通过全局敏感性分析方法评估不同水和N应力下作物N吸收和作物生长模拟的影响。冬小麦夏季玉米旋转三年实验数据采用了不同水和N管理实践来测试模型。结果表明,WHCNS模型在建模土壤水和矿物质N的情况下表现良好。在可接受的范围内,作物N吸收,赖,干物质和产量模拟的统计指标的价值在可接受的范围内,并且具有相对均方误差(rrmse)& 24%,协议指数(IA)> 0.81和纳什和Sutcliffe指数(NSE)> 0.37。然而,M2方法使用最小输入参数进行良好,因此建议在土壤作物模型中模拟作物N吸收。在这项研究中,高水位和N输入处理的数据集更适合于模型参数估计,以减少不确定性,中间和低水量和N个输入处理的数据集适合用于验证模型。这些信息对于指导选择建模方法和模型校准数据集是有用的。

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