张萌, 高艳梅, 张永清. 不同播种密度和灌溉管理下冬小麦APSIM NG模型的敏感性研究与模型校准验证[J]. 中国生态农业学报 (中英文), 2023, 31(1): 102−112. DOI: 10.12357/cjea.20220384
引用本文: 张萌, 高艳梅, 张永清. 不同播种密度和灌溉管理下冬小麦APSIM NG模型的敏感性研究与模型校准验证[J]. 中国生态农业学报 (中英文), 2023, 31(1): 102−112. DOI: 10.12357/cjea.20220384
ZHANG M, GAO Y M, ZHANG Y Q. Sensitivity analysis and calibration of the APSIM next-generation model under different irrigation and sowing density in wheat[J]. Chinese Journal of Eco-Agriculture, 2023, 31(1): 102−112. DOI: 10.12357/cjea.20220384
Citation: ZHANG M, GAO Y M, ZHANG Y Q. Sensitivity analysis and calibration of the APSIM next-generation model under different irrigation and sowing density in wheat[J]. Chinese Journal of Eco-Agriculture, 2023, 31(1): 102−112. DOI: 10.12357/cjea.20220384

不同播种密度和灌溉管理下冬小麦APSIM NG模型的敏感性研究与模型校准验证

Sensitivity analysis and calibration of the APSIM next-generation model under different irrigation and sowing density in wheat

  • 摘要: 以中国农业大学吴桥试验站2015—2018年度不同灌溉管理和播种密度下冬小麦为研究对象, 应用全局敏感性分析方法扩展傅里叶幅度检验法(EFAST)分析新一代的APSIM模型(APSIM Next-generation)中的品种参数对冬小麦生长的敏感性, 并实现模型参数的本地化调试与验证, 为该模型未来开展各项研究提供基础支持。结果表明, 影响冬小麦开花期和成熟期最敏感的参数是叶热间距、最小叶片数量、光周期敏感度; 影响产量最敏感的参数是开花期单位茎秆干重可孕花数、单籽粒库容潜力和叶热间距。根据敏感性分析的结果, 对以上参数进行优先校准后, 提高了APSIM NG模型对小麦生育时期和产量模拟的准确性, 校准后的模型可以解释生育时期超过98%的变异, 产量实测值和模拟值的均方根误差(RMSE)为508 kg∙hm−2

     

    Abstract: The Agricultural Production Systems Simulator (APSIM) is currently one of the most widely-used crop and farming system models globally. With increasing challenges and demand for agricultural modeling, the APSIM Initiative is building the next generation of APSIM to improve its prediction accuracy and increase its applicability to a wider range of farming systems. The APSIM next-generation (APSIM NG) model implemented new phenology- and morphology-simulating mechanisms, introduced additional parameters, and allowed modelers to add custom parameters. These parameters cannot be directly measured and must be calibrated when the APSIM NG model is applied to a new environment and cultivar. Determining the relative importance of the parameters to the specific outputs can streamline the calibration of crop models for new cultivars, and a sensitivity analysis can quantify the influence of model input parameters on model outputs. In this study, we used the Extended Fourier Amplitude Sensitivity Test to perform a sensitivity analysis on the wheat module of the APSIM NG for the first time. We also calculated the main and total effect sensitivity indices of three outputs — yield, flowering day, and maturity day — to crop parameters under different irrigation and plant density treatments. We found that days to anthesis and physiological maturity were mostly sensitive to the parameters that determine the length of the reproductive stages (phyllochron, number of leaves the plant will produce when fully vernalized early and grown in long photoperiod, and photoperiod sensitivity), and yield was most sensitive to the cultivar parameters that determine the yield component (GrainsPerGramOfStem, MaximumPotentialGrainSize) and phyllochron. Irrigation and sowing density treatments affected the main effect and total effect sensitivity index of parameters to yield; however, it did not affect the order of parameters. Next, we calibrated the model against data from 2015 to 2018 from the Wuqiao Experimental Station of the China Agricultural University in Hebei Province. The data comprise four irrigation and plant density treatments. The calibrated APSIM NG model captured the Zadoks decimal growth scale and yield with acceptable accuracy. Across the treatments, the APSIM NG explained more than 98% of the variation in the growth scale. The root-mean-square error (RMSE) of the yield was 508 kg∙hm2, compared with the experimental data. This study provides guidelines for APSIM NG model calibration in the North China Plain, as well as guidance to simplify the APSIM NG model and improve its precision, especially when many parameters are used. For robust phenology and yield prediction with APSIM NG, more research on the environment, genotype, and management factors is suggested.

     

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