基于APSIM模型旱地小麦叶面积指数相关参数的优化

doi:10.3864/j.issn.0578-1752.2019.12.004

Abstract

Abstract:

【Objective】The effective application of the model depends on the fast and accurate estimation of parameters. The current problems in the calibration of crop growth model parameters include large data volume, long time consumption, lack of precision, and low efficiency. The study tried to solve the problems. 【Method】Based on the field experimental data of two experimental sites (Mazichuan village, Lijiabaotown and Anjiagou village, Fengxiangtown) in Andingdistrict, Dingxi city in multiple years (2002-2005 and 2015-2017) and the meteorological data in Andingdistrict, Dingxicity from 1971 to 2017, the parameters related to dryland wheat leaf area index (LAI) in the APSIM (agricultural production systems simulator) model were optimized with the intelligent iteration search principle of shuffled frog leaping algorithm (SFLA) and tested by the correlation analysis method. 【Result】The biological evolution learning strategy of local depth search within sub-group and global information communication between sub-group in frog population, which was relatively independent and coordinated, was used to effectively improve the speed of calculation and realize the fast and accurate estimation of the parameters related to dryland wheat LAI in the APSIM model. The related parameters mainly included: The required thermal time interval for node appearance on the main stem, the initial node number at emergence, the initial leaf number at emergence, the initial leaf area index at emergence, the growing node number, and the maximum specific leaf area. LAI was respectively simulated by using the parameters based on the trial and error method and based on SFLA. After parameter optimization, the root mean square error (RMSE) between simulated and measured wheat LAI reduced from 0.069 to 0.027, the normalized root mean square error (NRMSE) decreased from 8.09% to 4.56%, and the model effective index (M_E) increased from 0.979 to 0.993. 【Conclusion】Compared with the trial and error method, which was usually used in the calibration of APSIM model, the intelligent iterative behavior with spontaneous learning characteristics based on the SFLA could realize automatic calibration of the parameters and improve the efficiency. The parameters estimated based on the SFLA could remarkably improve the simulation accuracy of wheat LAI. The application of SFLA was effective in calibrating crop models involving complex eco-physiological processes, and it could provide an effective parameter optimization method for improving the disadvantages in the model parameter calibration process include large data volume, long time consumption, lack of precision, and low efficiency.

Key words: APSIM, wheat, leaf area index, parameter optimization, shuffled frog leaping algorithm

NIE ZhiGang,LI Guang,WANG Jun,MA WeiWei,LUO CuiPing,DONG LiXia,LU YuLan. Parameter Optimization for the Simulation of Leaf Area Index of Dryland Wheat with the APSIM Model[J].Scientia Agricultura Sinica, 2019, 52(12): 2056-2068.

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References 41

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项目 Item	土层深度 Soil depth
项目 Item	5 cm	10 cm	30 cm	50 cm	80 cm	110 cm	140 cm	170 cm	200 cm
容重 BD (g·cm^-3）	1.29	1.23	1.32	1.20	1.14	1.14	1.13	1.12	1.11
萎蔫系数 WC (mm·mm^-1)	0.08	0.08	0.08	0.08	0.09	0.09	0.11	0.13	0.13
最大持水量 DUL (mm·mm^-1)	0.27	0.27	0.27	0.27	0.26	0.27	0.26	0.26	0.26
饱和水分含量 SM (mm·mm^-1)	0.46	0.49	0.45	0.50	0.52	0.52	0.48	0.53	0.53
风干系数 CA (mm·mm^-1)	0.01	0.01	0.05	0.07	0.07	0.07	0.07	0.07	0.07
土壤导水率 SWC (mm·h^-1)	0.60	0.60	0.60	0.60	0.60	0.60	0.60	0.60	0.60
小麦有效水分下限 LLW (mm·mm^-1)	0.09	0.09	0.09	0.09	0.09	0.10	0.11	0.13	0.15

参数 Parameter	默认值 Default value	优化值 Optimized value
主茎上节出现所需的热时间间隔 The required thermal timeinterval for node appearance on the main stem (°C·d)	95.0	98.5
小麦出苗后初始化的节数The initial node number at emergence	2.00	1.79
小麦出苗后初始化的叶片数The initial leaf number at emergence	2.00	1.66
小麦出苗后初始化的叶面积指数The initial leaf area index at emergence	0.080	0.074
某日正在生长的节数The growing node number	2.00	1.86
最大比叶面积The maximum specific leaf area (mm²·g^-1)	26000	h_SLA(LAI_d)

模型参数 Model parameter	麻子川村Mazichuan			安家沟村Anjiagou
模型参数 Model parameter	RMSE	NRMSE (%)	M_E	RMSE	NRMSE(%)	M_E
默认值 Default value	0.047	7.06	0.983	0.090	9.12	0974
优化值 Optimized value	0.031	4.53	0.993	0.023	4.59	0.992

Parameter Optimization for the Simulation of Leaf Area Index of Dryland Wheat with the APSIM Model

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