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Global sensitivity analysis of wheat grain yield and quality and the related process variables from the DSSAT-CERES model based on the extended Fourier Amplitude Sensitivity Test method

机译:基于扩展傅里叶振幅敏感性检验方法的DSSAT-CERES模型对小麦籽粒产量和品质的整体敏感性分析及相关过程变量

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摘要

A crop growth model, integrating genotype, environment, and management factor, was developed to serve as an analytical tool to study the influence of these factors on crop growth, production, and agricultural planning. A major challenge of model application is the optimization and calibration of a considerable number of parameters. Sensitivity analysis (SA) has become an effective method to identify the importance of various parameters. In this study, the extended Fourier Amplitude Sensitivity Test (EFAST) approach was used to evaluate the sensitivity of the DSSAT-CERES model output responses of interest to 39 crop genotype parameters and six soil parameters. The outputs for the SA included grain yield and quality (take grain protein content (GPC) as an indicator) at maturity stage, as well as leaf area index, aboveground biomass, and aboveground nitrogen accumulation at the critical process variables. The key results showed that: (1) the influence of parameter bounds on the sensitivity results was slight and less than the impacts from the significance of the parameters themselves; (2) the sensitivity parameters of grain yield and GPC were different, and the sensitivity of the interactions between parameters to GPC was greater than those between the parameters to grain yield; and (3) the sensitivity analyses of some process variables, including leaf area index, aboveground biomass, and aboveground nitrogen accumulation, should be performed differently. Finally, some parameters, which improve the model’s structure and the accuracy of the process simulation, should not be ignored when maturity output as an objective variable is studied.
机译:开发了一个整合了基因型,环境和管理因素的作物生长模型,以作为分析工具来研究这些因素对作物生长,生产和农业计划的影响。模型应用的主要挑战是大量参数的优化和校准。灵敏度分析(SA)已成为识别各种参数重要性的有效方法。在这项研究中,扩展的傅立叶振幅灵敏度测试(EFAST)方法用于评估感兴趣的DSSAT-CERES模型输出响应对39种作物基因型参数和6种土壤参数的敏感性。 SA的输出包括成熟阶段的谷物产量和品质(以谷物蛋白质含量(GPC)为指标),以及关键工艺变量下的叶面积指数,地上生物量和地上氮积累。关键结果表明:(1)参数范围对灵敏度结果的影响很小,且小于参数本身意义的影响。 (2)籽粒产量和GPC的敏感性参数不同,各参数之间对GPC的相互作用的敏感性大于各参数之间对谷物产量的敏感性。 (3)对某些过程变量的敏感性分析,包括叶面积指数,地上生物量和地上氮积累,应以不同的方式进行。最后,在研究成熟度输出作为目标变量时,不应忽略某些改善模型结构和过程仿真精度的参数。

著录项

  • 来源
    《农业科学学报(英文版)》 |2019年第7期|1547-1561|共15页
  • 作者单位

    Key Laboratory of Agri-Informatics, Ministry of Agriculture and Rural Affairs, Beijing 100097, P.R.China;

    Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, P.R.China;

    UMR EMMAH, INRA, NAPV, Avignon 84914, France;

    Agricultural Information Institute, Chinese Academy of Agricultural Sciences/Key Laboratory of Agri-Information Service Technology, Ministry of Agriculture and Rural Affairs, Beijing 100081, P.R.China;

    Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, P.R.China;

    Beijing Research Center for Agri-Food Testing and Farmland Monitoring, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, P.R.China;

  • 收录信息 中国科学引文数据库(CSCD);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
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