Scientia Agricultura Sinica ›› 2025, Vol. 58 ›› Issue (3): 537-547.doi: 10.3864/j.issn.0578-1752.2025.03.010

• SOIL & FERTILIZER·WATER-SAVING IRRIGATION·AGROECOLOGY & ENVIRONMENT • Previous Articles     Next Articles

Optimization of N2O Emission Parameters in Dryland Spring Wheat Farmland Soil Based on Whale Optimization Algorithm

MU ShuJia1(), DONG LiXia1(), LI Guang2, YAN ZhenGang1, LU YuLan1   

  1. 1 School of Information Science and Technology, Gansu Agricultural University, Lanzhou 730070
    2 School of Forestry, Gansu Agricultural University, Lanzhou 730070
  • Received:2024-03-13 Accepted:2024-05-28 Online:2025-02-01 Published:2025-02-11
  • Contact: DONG LiXia

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Abstract:

【Objective】In order to improve the simulation accuracy of N2O emissions by using APSIM model, this study used Whale Optimization Algorithm (WOA) to optimize the default parameters related to soil N2O emissions in the APSIM model to improve the accuracy and applicability of the model in simulating soil N2O emissions in the semi-arid agricultural region of northwest China, for providing support for precise assessment and management of greenhouse gas emissions in agricultural activities. 【Method】This study used field experimental data measured by the Anjiapo integrated long-term positioning test station in Anding District, Dingxi City, Gansu Province from 2020 to 2021, combined with meteorological data provided by the Meteorological Bureau from 1970 to 2021, to optimize the four key parameters of N2O formation stage in the APSIM model (soil nitrification potential (nitration_pot), concentration of ammonium nitrogen at semi maximum utilization efficiency (nh4_at-half_pot), denitrification coefficient (dnit_rate-coeff), and power term of denitrification water coefficient (dnit_wf_power) using the WOA for single objective and multi parameter optimization. The accuracy of the optimized APSIM soil N2O emission model was evaluated by comparing the errors between the default parameter simulation values, optimized parameter simulation values, and measured values of the APSIM model. 【Result】Through multiple executions of the optimization program, the optimal combination of four parameters was ultimately determined. Among them, the soil nitrification potential was 7.62 mg·kg-1·d-1, the concentration of ammonia nitrogen at semi maximum utilization efficiency was 49.3 mg·kg-1, the denitrification coefficient was 0.00063, and the power term of the denitrification water coefficient calculation was 0.64. Compared with the default parameters of the APSIM model, the coefficient of determination R2 increased from 0.432 to 0.719, the root mean square error (RMSE) decreased from 39.42 to 25.37 μg·m-2·h-1, and the normalized root mean square error (NRMSE) decreased from 18.51% to 11.92%. The whale algorithm exhibited significant global search capability and fast convergence during the optimization process. The optimized APSIM model significantly improved the accuracy of simulating soil N2O emissions, indicating that this method could achieve rapid and accurate calibration of model parameters. 【Conclusion】By applying WOA, four key parameters were precisely adjusted, which significantly reduced the prediction error of the model and significantly improving the performance of the APSIM soil N2O emission model. The optimized model has shown higher accuracy and applicability in the semi-arid agricultural region of northwest China, which also proved the effectiveness of the optimization strategy.

Key words: N2O emission, APSIM model, parameter optimization, Whale Optimization Algorithm (WOA), spring wheat, dryland soil

Table 1

Soil parameters in the experimental area"

参数
Parameter		 土壤深度 Soil depth (cm)
0-5 5-10 10-30 30-50 50-80 80-110 110-140 140-170 170-200
容重Bulk density (g·cm-3) 1.29 1.23 1.33 1.20 1.14 1.14 1.25 1.12 1.11
风干含水量 Air-dried moisture (mm·mm-1) 0.01 0.01 0.05 0.07 0.09 0.10 0.11 0.12 0.13
萎蔫系数 Wilting coefficient (mm·mm-1) 0.09 0.09 0.09 0.09 0.09 0.11 0.11 0.12 0.13
田间持水量 Field capacity (mm·mm-1) 0.27 0.27 0.27 0.27 0.26 0.27 0.26 0.26 0.26
饱和含水量 Saturated moisture (mm·mm-1) 0.46 0.49 0.45 0.50 0.52 0.52 0.48 0.53 0.53
有效水分下限 Lower available moisture (mm·mm-1) 0.09 0.09 0.09 0.09 0.10 0.12 0.13 0.18 0.2

Fig. 1

Whale Algorithm process"

Table 2

Upper and lower limits of parameters to be optimized"

参数Parameter 默认值Default 范围Range
土壤硝化潜力Soil nitrification potential (mg·kg-1·d-1) 40 6—60
铵态氮在半最大利用效率时的浓度
The concentration of ammonia nitrogen at semi maximum utilization efficiency (mg·kg-1)		 90 6—186
反硝化系数Denitrification coefficient 0.0006 0.0005—0.0018
反硝化水系数计算的幂项P Power term P for calculating denitrification water coefficient 1 0.5—5

Fig. 2

Trend chart of four parameter changes"

Fig. 3

Trend chart of fitness function changes"

Table 3

Comparison between default and optimized values of model parameters to be optimized"

参数Parameter 默认值Default 优化值Optimization
土壤硝化潜力Soil nitrification potential (mg·kg-1·d-1) 40 7.62
铵态氮在半最大利用效率时的浓度
The concentration of ammonia nitrogen at semi maximum utilization efficiency (mg·kg-1)		 90 49.3
反硝化系数Denitrification coefficient 0.0006 0.00063
反硝化水系数计算的幂项P Power term P for calculating denitrification water coefficient 1 0.64

Table 4

Comparison of results before and after parameter optimization"

模型参数
Model parameter		 决定系数
R2		 均方根误差
RMSE (μg·m-2·h-1)		 归一化均方根误差
NRMSE (%)
默认值Default 0.423 39.42 18.51
优化后After optimization 0.719 25.37 11.92

Fig. 4

Comparison of default, measured, and optimized N2O emissions"

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