...
首页> 外文期刊>Agricultural and Forest Meteorology >Sensitivity of simulated crop yield and nitrate leaching of the wheat-maize cropping system in the North China Plain to model parameters
【24h】

Sensitivity of simulated crop yield and nitrate leaching of the wheat-maize cropping system in the North China Plain to model parameters

机译:北中国麦玉米种植系统模拟作物产量和硝酸盐浸出的敏感性

获取原文
获取原文并翻译 | 示例
           

摘要

Process-based crop simulation models are often over-parameterised and are therefore difficult to calibrate properly. Following this rationale, the Morris screening sensitivity method was carried out on the DAISY model to identify the most influential input parameters operating on selected model outputs, i.e. crop yield, grain nitrogen (N), evapotranspiration and N leaching. The results obtained refer to the winter wheat-summer maize cropping system in the North China Plain. In this study, four different N fertiliser treatments over six years were considered based on a randomised field experiment at Luancheng Experimental Station to elucidate the impact of weather and nitrogen inputs on model sensitivity. A total of 128 parameters were considered for the sensitivity analysis. The ratios [output changes/parameter increments] demonstrated high standard deviations for the most relevant parameters, indicating high parameter non-linearity/interactions. In general, about 34 parameters influenced the outputs of the DAISY model for both crops. The most influential parameters depended on the output considered with sensitivity patterns consistent with the expected dominant processes. Interestingly, some parameters related to the previous crop were found to affect output variables of the following crop, illustrating the importance of considering crop sequences for model calibration. The developed RDAISY toolbox used in this study can serve as a basis for following sensitivity analysis of the DAISY model, thus enabling the selection of the most influential parameters to be considered with model calibration.
机译:基于过程的作物仿真模型通常是过度参数化的,因此难以正确校准。在这一理由之后,在菊花模型上进行Morris筛选灵敏度方法,以识别在所选模型输出上运行的最有影响力的输入参数,即作物产量,晶粒氮(N),蒸发蒸腾和N浸出。得到的结果是指华北平原冬小麦夏季玉米种植制度。在这项研究中,基于栾城实验站的随机田间实验,考虑了四种不同的氮肥治疗,以阐明天气和氮气投入对模型敏感性的影响。对于灵敏度分析,总共考虑了128个参数。比率[输出更改/参数增量]展示了最相关参数的高标准偏差,指示高参数非线性/交互。通常,大约34个参数影响了两种作物的菊花模型的产出。最有影响力的参数取决于与敏感模式考虑的输出,与预期的主导过程一致。有趣的是,发现与先前作物相关的一些参数影响以下作物的输出变量,说明考虑模型校准的裁剪序列的重要性。本研究中使用的已开发的RDAISY工具箱可以作为以下对菊花模型进行敏感性分析的基础,从而能够选择要考虑的模型校准的最有影响力的参数。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号