Water and nitrogen are the key influencing factors on the N2O emissions in the farmland ecosystem cycle, particularly in the dry wheat fields. However, it is often required for the accurate and rapid analysis of the parameter sensitivity in the crop model, in order to enhance the model efficiency and application. This study aims to explore the sensitivity of water and nitrogen factors to the output variables, including the N2O emission parameters in the agricultural production systems simulator (APSIM) crop model. The extended Fourier amplitude sensitivity Test (EFAST) was employed to verify the simulation. The spring wheat variety, 'Dingxi 35' was taken as the research subject. Five gradients of nitrogen application were set as 0, 55, 110, 150, and 220 kg/hm2. Additionally, three gradients of supplemental irrigation were set as 0, 50, and 100 mm. A global sensitivity analysis was conducted on the nine parameters of the crop variety, thirteen soil parameters, and four meteorological parameters related to the soil N2O emissions in dryland wheat fields within the APSIM model. The results indicated that the photoperiodic sensitivity index (PS) was the most sensitive parameter to the N2O emissions under different water and nitrogen treatments. The average values in the global sensitivity index of the PS were 0.518, 0.548, and 0.57, respectively. Their sensitivity decreased with the increasing nitrogen application, whereas, there was an increase with the supplemental irrigation. Furthermore, the soil bulk density (BD) was identified as the most sensitive parameter on the N2O emissions in dry wheat fields under water and nitrogen conditions. The average values in the global sensitivity index of the BD were 0.324, 0.301, and 0.427, respectively. Overall, their sensitivity increased with the higher nitrogen application rates and supplemental irrigation. The maximum daily temperature (Max) was found to be the most sensitive meteorological parameter to the N2O emissions. The average values in the global sensitivity index of Max were 0.554, 0.477, and 0.537, respectively. Their sensitivity generally decreased with the increasing nitrogen application rates, but there was a trend of the first decreasing and then increasing with the supplemental irrigation rates. Notably, there was a great variation in the meteorological parameters on the much smaller values of the N2O flux output in the APSIM model, compared with the crop variety and soil parameters. This finding can provide valuable insights into the sensitivity contributions of the soil N2O emission parameters in the APSIM model. A theoretical basis can also offer a strong reference to calibrate the parameters for applicability and simulation accuracy in the study area. Additionally, the scientific foundation can be gained for water and nitrogen management in dry wheat fields.
- Article type
- Year
- Co-author
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.
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.
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.
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.
京公网安备11010802044758号