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Acta Agronomica Sinica ›› 2018, Vol. 44 ›› Issue (8): 1229-1236.doi: 10.3724/SP.J.1006.2018.01229

• TILLAGE & CULTIVATION·PHYSIOLOGY & BIOCHEMISTRY • Previous Articles     Next Articles

Parameter Optimization in APSIM-Based Simulation Model for Yield Formation of Dryland Wheat Using Shuffled Frog Leaping Algorithm

Zhi-Gang NIE1,2(),Guang LI3,*(),Cui-Ping LUO2,Wei-Wei MA3,Yong-Qiang DAI2   

  1. 1 College of Resource and Environment Science, Gansu Agricultural University, Lanzhou 730070, Gansu, China
    2 College of Information Science and Technology, Gansu Agricultural University, Lanzhou 730070, Gansu, China
    3 College of Forestry, Gansu Agricultural University, Lanzhou 730070, Gansu, China
  • Received:2017-07-27 Accepted:2018-03-26 Online:2018-08-10 Published:2018-04-24
  • Contact: Guang LI E-mail:niezg@gsau.edu.cn;lig@gsau.edu.cn
  • Supported by:
    the National Natural Science Foundation of China(31660348);the National Natural Science Foundation of China(31560378);the National Natural Science Foundation of China(31560343);the Youth Tutor Foundation of Gansu Agricultural University(GAU-QNDS-201701)

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Abstract:

The rapid and accurate estimation of model parameters is an important prerequisite for the application of yield formation model. In the process of localization parameters calibration for yield formation based on APSIM (agricultural production systems simulator) model of dryland wheat, there are some deficiencies such as large scale, long time consuming, a lack of precision and low efficiency. In this study, intelligent algorithm was used to remedy the deficiencies. We collected and analyzed the field experimental data in Mazichuan village, Lijiabao town, Anding district, Dingxi city from 2002 to 2005, and Anjiagou village, Fengxiang town, Anding district, Dingxi city from 2015 to 2016, and the historical and meteorological data in Anding district, Dingxi city from 1971 to 2016. According to the characteristics of the yield formation model for parameters nonlinearity and multidimensional change, making full use of the intelligent strategy of advanced group rotation and global information exchange in shuffled frog leaping algorithm and the self-organization, self-learning intelligent algorithm characteristics, the estimation parameters more difficult to obtain in the model of the dryland wheat yield formation based on APSIM platform were optimized and tested by correlation analysis method. This optimization method could use frog intelligent group biology evolution learning strategy to estimate the yield formation model parameters of dryland wheat. Compared with the method of attempting to eliminate the error, which is used in the localization parameters calibration of APSIM platform usually, the accuracy of simulation output was significantly improved. The root mean square error (RMSE) reduced from 79.13 kg ha -1 to 35.36 kg ha -1, the normalized root mean square error (NRMSE) decreased from 5.97% to 2.63%, and the model effectiveness index (ME) increased from 0.939 to 0.989. This method has strong global optimization ability, reasonable calculation quantity, and fast convergence speed.

Key words: Wheat, Shuffled frog leaping algorithm, APSIM, Parameters optimization

Table 1

Soil property parameters in the experiment site used for specifying APSIM platform and lower water limit of wheat"

项目
Item		 土层深度 Soil depth
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

Fig. 1

SFLA-based parameters optimization process for wheat yield formation model based on APSIM"

Table 2

Comparison between the SFLA-based optimized and default values in wheat yield formation model"

参数
Parameter		 默认值
Default value		 优化值
Optimized value
每克茎籽粒数
Grain number per gram stem (grain g-1)		 25 26
开花到灌浆开始日潜在籽粒平均灌浆速率
Daily potential rate of grain filling from flowering to start of grain filling (g grain-1)		 0.00100 0.00112
灌浆期日潜在籽粒平均灌浆速率
Daily potential rate of grain filling during grain filling (g grain-1)		 0.00200 0.00249
日潜在籽粒平均氮积累速率
Daily potential rate of nitrogen accumulation (g grain-1)		 0.000055 0.000067
日籽粒氮积累速率下限
Minimum rate of nitrogen accumulation (g grain-1)		 0.000015 0.000018
单株最大籽粒干重
Maximum grain dry weight per plant (g)		 0.0400 0.0437

Fig. 2

Relationship of simulated and observed values of wheat yield in dryland"

Table 3

Test results of simulation on wheat yield formation model before and after parameters optimization"

模型参数
Model parameter		 麻子川村 Mazichuan 安家沟村 Anjiagou
RMSE (kg hm-2) NRMSE (%) ME RMSE (kg hm-2) NRMSE (%) ME
默认值 Default value 64.21 4.33 0.966 94.05 7.61 0.912
优化值 Optimized value 33.64 2.27 0.991 36.88 2.99 0.986
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