Sensitivity analysis and optimization of leaf area index related parameters of dryland wheat based on APSIM model
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摘要: 为解决作物模型参数率定过程中参数众多导致的敏感参数定位迟缓和调参效率低的问题, 本研究运用敏感性分析和智能优化算法相结合的方法对作物模型参数进行调整, 以甘肃省定西市安定区李家堡镇麻子川村(2002—2004年)和凤翔镇安家沟村(2015—2017年)大田旱地小麦试验数据(叶面积指数)为参照, 利用扩展傅里叶幅度检验法(EFAST), 对APSIM-Wheat旱地小麦叶片生长子模型的23个参数进行敏感性分析, 得到对模型结果较敏感的部分参数, 然后利用粒子群优化算法对部分敏感参数进行优化。结果表明: 1)影响旱地小麦叶片生长最敏感的参数依次为叶面积指数为0时最大比叶面积、叶片生长的氮限制因子、出苗到拔节积温、消光系数、拔节到开花积温、蒸腾效率系数; 2)旱地小麦叶片生长子模型的参数优化结果: 叶面积指数为0时最大比叶面积为26 652 mm2∙g−1, 叶片生长的氮限制因子为0.96, 出苗到拔节积温为382 ℃·d, 消光系数为0.44, 拔节到开花积温为542 ℃·d, 蒸腾效率系数为0.0056; 3)上述参数优化后的叶面积指数实测值与模拟值之间的均方根误差平均值从参数优化前的0.080减小到0.042, 归一化均方根误差平均值从11.54%减小到6.11%, 模型有效性指数平均值从0.962增加到0.988, 优化后叶面积指数的模拟更好。该方法相对于传统的手工试错法, 避免了优化参数的不确定性, 实现参数自动率定, 提高模型参数的率定效率, 有利于模型快速地本地化应用, 并指导农业生产。本研究方法也对APSIM-Wheat模型中其他作物模块的参数调整优化具有指导意义。Abstract: Crop growth model parameterization is characterized by a large number of parameters and the low efficiency of parameterization. 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 Dingxi 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 sensitivity 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 temperature from jointing to flowering, and transpiration efficiency coefficient; 2) optimization of the parameters in the leaf growth submodel 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 accumulated temperature from jointing to flowering was 542 ℃·d, and a transpiration efficiency coefficient was 0.0056; 3) after the optimization 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 uncertainty 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 efficiency in the process of model rate fixing, and finally, enables the model to be applied locally faster so that it can better guide the agricultural production. The methodology of this study is also instructive for the parameter tuning optimization of other crop modules in the APSIM-Wheat model.
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图 2 APSIM-Wheat旱地小麦叶面积指数优化前和优化后与实测值的关系
Figure 2. Relationship between pre- and post-optimization and measured values of leaf area index of APSIM-Wheat dryland wheat
土层
Soil layer
(mm)容重
Bulk density (g∙cm−3)最大持水量
Field capacity
(mm∙mm−1)萎蔫系数
Wilting coefficient (mm∙mm−1)风干系数
Coefficient of air-dry (mm∙mm−1)饱和水含量
Saturated water content (mm∙mm−1)土壤导水率
Soil hydraulic conductivity (mm∙h−1)有效水分下限
Lower limit of effective mositure (mm∙mm−1)0~50 1.29 0.27 0.08 0.01 0.46 0.60 0.09 50~100 1.23 0.27 0.08 0.01 0.49 0.60 0.09 100~300 1.32 0.27 0.08 0.05 0.45 0.60 0.09 300~500 1.20 0.27 0.08 0.07 0.50 0.60 0.09 500~800 1.14 0.26 0.09 0.07 0.52 0.60 0.09 800~1100 1.14 0.27 0.09 0.07 0.52 0.60 0.10 1100~1400 1.13 0.26 0.11 0.07 0.48 0.60 0.11 1400~1700 1.12 0.26 0.13 0.07 0.53 0.60 0.13 1700~2000 1.11 0.26 0.13 0.07 0.53 0.60 0.15 
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表 2 APSIM-Wheat旱地小麦叶片生长子模型的23个品种参数及其上下限
Table 2. Parameters of 23 varieties and their upper and lower limits of the APSIM-Wheat leaf growth submodel for dryland wheat
参数
Parameter定义
Definition下限值
Lower bound上限值
Upper boundPhotop_sens 作物光周期敏感性指数 Crop photoperiodic sensitivity index 0 5 Vern_sens 作物春化敏感性指数 Crop vernalization sensitivity index 0 5 y_rue 出苗到灌浆结束的辐射利用效率
Radiation use efficiency from seedling emergence to the end of grouting (g∙MJ−1)1.1160 1.3640 y_extinct_coef 消光系数 Extinction coefficient (k) 0.25 0.75 node_no_correction 叶鞘中正在生长的叶数 Number of growing leaves in leaf sheaths 1 3 leaf_no_at_emerg 出苗时的叶片数量 Number of leaves at emergence 1 3 initial_tpla 初始叶面积 Initial leaf area (mm2∙plant−1) 100 300 min_tpla 最小叶面积 Minimum leaf area (mm2∙plant−1) 2.5 7.5 y_sla_max0 叶面积指数为0时最大比叶面积 Maximum specific leaf area at a leaf area index of 0 (mm2∙g−1) 13 500 40 500 y_sla_max5 叶面积指数为5时最大比叶面积 Maximum specific leaf area at a leaf area index of 5 (mm2∙g−1) 11 000 33 000 tt_end_of_juvenile 出苗到拔节积温 Accumulated temperature from seedling to jointing (℃∙d) 200 600 tt_floral_initiation 拔节到开花积温 Accumulated temperature from jointing to flowering (℃∙d) 250 800 tt_flowering 开花到灌浆积温 Accumulated temperature from flowering to grouting (℃∙d) 60 180 tt_start_grain_fill 灌浆到成熟积温 Accumulated temperature from grouting to maturity (℃∙d) 200 900 y_node_no_rate 节点出现的热时间间隔 Thermal time interval for node appearance (℃∙d) 47.5 142.5 transp_eff_cf 蒸腾效率系数 Transpiration efficiency coefficient 0.003 0.009 fr_lf_sen_rate 主茎和节点上总叶片老化比例 Proportion of total leaves aging on main stems and nodes 0.0175 0.0525 sen_rate_water 光合叶片老化的水分胁迫斜率 Water stress slopes in photosynthetic leaf aging 0.005 0.01 sen_light_slope 遮阴导致叶面积老化敏感性系数 Sensitivity coefficient of leaf area aging due to shading 0.0010 0.0030 lai_sen_light 遮阴导致老化的最大叶面积指数
Maximum leaf area index for shade-induced deterioration (m2∙m−2)3.5 10.5 node_sen_rate 主茎上的节点老化率 Node aging rate on the main stem (℃∙d∙node−1) 30 90 N_fact_expansion 叶片生长的氮限制因子 Nitrogen limiting factors during leaf growth 0 1 N_fact_photo 氮亏缺对光合作用的影响系数 Coefficient of effect of nitrogen deficit on photosynthesis 0.75 2.25 transp_eff_cf是计算蒸腾效率时用到的系数, 并非广义的蒸腾效率系数。transp_eff_cf is the coefficients used in the calculation of transpiration efficiency and it is not generalized coefficients of transpiration efficiency.
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表 3 APSIM-Wheat旱地小麦叶片生长子模型相关参数的初值及优化值
Table 3. Initial and optimized values of parameters related to the APSIM-Wheat leaf growth submodel for dryland wheat
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表 4 APSIM-Wheat旱地小麦叶片生长子模型的小麦叶面积指数模拟检验结果
Table 4. Results of the simulation test of leaf area index of dryland wheat using APSIM-Wheat leaf growth sub-model
参数 Parameter 麻子川村 Mazichuan Village 安家沟村 Anjiagou Village RMSE NRMSE (%) ME RMSE NRMSE (%) ME 默认值 Default value 0.070 10.53 0.968 0.090 12.55 0.956 优化值 Optimized value 0.038 5.74 0.989 0.046 6.47 0.987 RMSE为均方根误差; NRMSE为归一化均方根误差; ME为模型有效性指数。RMSE is root mean square error; NRMSE is normalized root mean square error; ME is the model validity index.
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