中国生态农业学报（中英文）2024，Vol.32Issue(1)：119-129,11.DOI:10.12357/cjea.20230345
基于APSIM模型的旱地小麦叶面积指数相关参数敏感性分析及优化
Sensitivity analysis and optimization of leaf area index related parameters of dryland wheat based on APSIM model
摘要
Abstract
Crop growth model parameterization is characterized by a large number of parameters and the low efficiency of parameter-ization.To determine the rate of crop model parameters quickly and efficiently,the promotion of rapid application of crop models in localization is required.In this study,we used a combination of sensitivity analysis and intelligent optimization algorithm to adjust the parameters of the crop model.We used the experimental data(leaf area index)of dryland wheat in large fields in Mazichuan Village,Lijiabao Town from 2002 to 2004,and Anjiagou Village,Fengxiang Town from 2015 to 2017 in Anding District,Dingxi City,Gansu Province as references.Using the extended Fourier amplitude sensitivity test method,a sensitivity analysis of 23 parameters of the APSIM-Wheat dryland wheat leaf growth sub-model was performed using SimLab software,and the sensitivity coefficients of each parameter to the model results were obtained.On this basis,the parameters with a larger first-order sensitivity index and global sensit-ivity index were selected as the optimization parameters,and R programming was used to construct the algorithmic fitness function,implement the particle swarm optimization algorithm,and run the APSIM-Wheat model to optimize the parameters automatically.We performed this to ensure fast and effective determination of the model parameters.The results showed that:1)the six parameters most sensitive to the leaf growth model of dryland wheat were,in descending order,maximum specific leaf area at a leaf area index of 0,nitrogen limiting factors in leaf growth,accumulated temperature from seedling to jointing,extinction coefficient,accumulated tem-perature from jointing to flowering,and transpiration efficiency coefficient;2)optimization of the parameters in the leaf growth sub-model for dryland wheat resulted in a maximum specific at a leaf area index of 0 was 26 652 mm2·g-1,a nitrogen limiting factor in leaf growth was 0.96,an accumulated temperature from seedling to jointing was 382 ℃·d,an extinction coefficient was 0.44,an accumu-lated temperature from jointing to flowering was 542 ℃·d,and a transpiration efficiency coefficient was 0.0056;3)after the optimiza-tion of the aforementioned parameters,the mean value of the root mean square error between the measured and simulated values of the leaf area index decreased from 0.080 to 0.042.The mean value of the normalized root mean square error decreased from 11.54％to 6.11％,and the mean value of the model validity index increased from 0.962 to 0.988,indicating that the simulation of the leaf area index was better after the optimization.When compared with the traditional manual trial-and-error method,this method avoids the un-certainty of the optimization parameters,quickly and efficiently identifies the important parameters of the model,realizes automatic parameter rate fixing,improves the efficiency of model parameter rate fixing,alleviates the problem of many parameters and low effi-ciency in the process of model rate fixing,and finally,enables the model to be applied locally faster so that it can better guide the ag-ricultural production.The methodology of this study is also instructive for the parameter tuning optimization of other crop modules in the APSIM-Wheat model.关键词
旱地小麦/APSIM-Wheat模型/全局敏感性分析/模型参数优化/EFAST方法/粒子群算法Key words
Dryland wheat/APSIM-Wheat model/Global sensitivity analysis/Model parameter optimization/EFAST method/Particle swarm optimization分类
农业科学引用本文复制引用
魏学厚,聂志刚..基于APSIM模型的旱地小麦叶面积指数相关参数敏感性分析及优化[J].中国生态农业学报（中英文）,2024,32(1):119-129,11.基金项目
国家自然科学基金项目(32160416)、甘肃省教育厅产业支撑计划(2021CYZC-15,2022CYZC-41)和甘肃农业大学青年导师扶持基金(GAU-QDFC-2022-19)资助 This study was supported by the National Natural Science Foundation of China(32160416),Gansu Provincial Education Department Industrial Sup-port Plan Project(2021CYZC-15,2022CYZC-41),and Gansu Agricultural University Youth Mentor Support Fund(GAU-QDFC-2022-19). （32160416）
