基于鲸鱼算法的旱地春小麦农田土壤N2O排放模型参数优化

doi:10.3864/j.issn.0578-1752.2025.03.010

Abstract

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

MU ShuJia, DONG LiXia, LI Guang, YAN ZhenGang, LU YuLan. Optimization of N₂O Emission Parameters in Dryland Spring Wheat Farmland Soil Based on Whale Optimization Algorithm[J].Scientia Agricultura Sinica, 2025, 58(3): 537-547.

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References 31

[1]	李军. 农业信息技术. 2版. 北京: 科学出版社, 2010: 1-17.
	LI J. Agricultural Information Technology. 2nd ed. Beijing: Science Press, 2010: 1-17. (in Chinese)
[2]	MYRGIOTIS V, WILLIAMS M, TOPP C F E, REES R M. Improving model prediction of soil N₂O emissions through Bayesian calibration. The Science of the Total Environment, 2018, 624: 1467-1477.
[3]	LI T T, ZHANG W, WANG J, ZHANG W, WANG G C, XU J J, ZHANG Q. Parameterizing an agricultural production model for simulating nitrous oxide emissions in a wheat-maize system in the North China Plain. Atmospheric and Oceanic Science Letters, 2016, 9(6): 403-410.
[4]	ZHANG Y J, YU Q. Does agroecosystem model improvement increase simulation accuracy for agricultural N₂O emissions? Agricultural and Forest Meteorology, 2021, 297: 108281.
[5]	聂志刚, 李广, 雒翠萍, 马维伟, 代永强. 利用混合蛙跳算法优化基于APSIM的旱地小麦产量形成模型参数. 作物学报, 2018, 44(8): 1229-1236. doi: 10.3724/SP.J.1006.2018.01229
	NIE Z G, LI G, LUO C P, MA W W, DAI Y Q. Parameter optimization in APSIM-based simulation model for yield formation of dryland wheat using shuffled frog leaping algorithm. Acta Agronomica Sinica, 2018, 44(8): 1229-1236. (in Chinese)
[6]	崔炜楠, 聂志刚, 李广, 王钧. 基于改进的混合蛙跳算法对旱地小麦籽粒生长模型参数的优化. 中国农业科学, 2023, 56(12): 2274-2287. doi: 10.3864/j.issn.0578-1752.2023.12.004.
	CUI W N, NIE Z G, LI G, WANG J. Optimization of dryland wheat grain growth model parameters based on an improved shuffled frog leaping algorithm. Scientia Agricultura Sinica, 2023, 56(12): 2274-2287. doi: 10.3864/j.issn.0578-1752.2023.12.004. (in Chinese)
[7]	张博, 董莉霞, 李广, 燕振刚, 刘强, 王钧, 张燕. 基于混沌万有引力算法对APSIM模型中旱地春小麦产量形成参数的优化. 麦类作物学报, 2024, 44(7): 919-925.
	ZHANG B, DONG L X, LI G, YAN Z G, LIU Q, WANG J, ZHANG Y. Optimization of dryland spring wheat yield formation parameters in APSIM model based on chaotic gravitational algorithm. Journal of Triticeae Crops, 2024, 44(7): 919-925. (in Chinese)
[8]	王钧, 李广, 闫丽娟, 刘强, 聂志刚. 旱地春小麦产量对不同生育阶段温度变化的响应模拟. 中国农业科学, 2020, 53(5): 904-916. doi: 10.3864/j.issn.0578-1752.2020.05.004.
	WANG J, LI G, YAN L J, LIU Q, NIE Z G. Simulation of spring wheat yield response to temperature changes of different growth stages in drylands. Scientia Agricultura Sinica, 2020, 53(5): 904-916. doi: 10.3864/j.issn.0578-1752.2020.05.004. (in Chinese)
[9]	李广, 黄高宝, William Bellotti, 陈文. APSIM模型在黄土丘陵沟壑区不同耕作措施中的适用性. 生态学报, 2009, 29(5): 2655-2663.
	LI G, HUANG G B, BELLOTTI W, CHEN W. Adaptation research of APSIM model under different tillage systems in the Loess hill-gullied region. Acta Ecologica Sinica, 2009, 29(5): 2655-2663. (in Chinese)
[10]	姚瑶, 李广, 王钧, 杨传杰, 张世康, 刘帅楠. 不同耕作措施下旱作春小麦农田二氧化碳排放模拟及敏感性分析. 干旱地区农业研究, 2021, 39(3): 171-178.
	YAO Y, LI G, WANG J, YANG C J, ZHANG S K, LIU S N. Simulation and sensitivity analysis of carbon dioxide emissions under different tillage systems in dryland spring wheat fields. Agricultural Research in the Arid Areas, 2021, 39(3): 171-178. (in Chinese)
[11]	HOLZWORTH D P, HUTH N I, DEVOIL P G, ZURCHER E J, HERRMANN N I, MCLEAN G, CHENU K, VAN OOSTEROM E J, SNOW V, MURPHY C, MOORE A D, BROWN H, WHISH J P M, VERRALL S, FAINGES J, BELL L W, PEAKE A S, POULTON P L, HOCHMAN Z, THORBURN P J, GAYDON D S, DALGLIESH N P, RODRIGUEZ D, COX H, CHAPMAN S, DOHERTY A, TEIXEIRA E, SHARP J, CICHOTA R, VOGELER I, LI F Y, WANG E L, HAMMER G L, ROBERTSON M J, DIMES J P, WHITBREAD A M, HUNT J, VAN REES H, MCCLELLAND T, CARBERRY P S, HARGREAVES J N G, MACLEOD N, MCDONALD C, HARSDORF J, WEDGWOOD S, KEATING B A. APSIM-Evolution towards a new generation of agricultural systems simulation. Environmental Modelling & Software, 2014, 62: 327-350.
[12]	WANG E, ROBERTSON M J, HAMMER G L, CARBERRY P S, HOLZWORTH D, MEINKE H, CHAPMAN S C, HARGREAVES J N G, HUTH N I, MCLEAN G. Development of a generic crop model template in the cropping system model APSIM. European Journal of Agronomy, 2002, 18(1/2): 121-140.
[13]	BERGHUIJS H N C, WEIH M, VAN DER WERF W, KARLEY A J, ADAM E, VILLEGAS-FERNÁNDEZ Á M, KIÆR L P, NEWTON A C, SCHERBER C, TAVOLETTI S, VICO G. Calibrating and testing APSIM for wheat-faba bean pure cultures and intercrops across Europe. Field Crops Research, 2021, 264: 108088.
[14]	CHAKI A K, GAYDON D S, DALAL R C, BELLOTTI W D, GATHALA M K, HOSSAIN A, MENZIES N W. How we used APSIM to simulate conservation agriculture practices in the rice-wheat system of the Eastern Gangetic Plains. Field Crops Research, 2022, 275: 108344.
[15]	MOHANTY M, SINHA N K, SOMASUNDARAM J, MCDERMID S S, PATRA A K, SINGH M, DWIVEDI A K, REDDY K S, RAO C S, PRABHAKAR M, HATI K M, JHA P, SINGH R K, CHAUDHARY R S, KUMAR S N, TRIPATHI P, DALAL R C, GAYDON D S, CHAUDHARI S K. Soil carbon sequestration potential in a Vertisol in central India- results from a 43-year long-term experiment and APSIM modeling. Agricultural Systems, 2020, 184: 102906.
[16]	LUO Z K, WANG E L, XING H T, SMITH C, WANG G C, CRESSWELL H. Opportunities for enhancing yield and soil carbon sequestration while reducing N₂O emissions in rainfed cropping systems. Agricultural and Forest Meteorology, 2017, 232: 400-410.
[17]	WANG G C, LUO Z K, WANG E L, ZHANG W. Reducing greenhouse gas emissions while maintaining yield in the croplands of Huang-Huai-Hai Plain, China. Agricultural and Forest Meteorology, 2018, 260/261: 80-94.
[18]	王良. 黄淮海小麦-玉米周年农田土壤N₂O排放动态分析及施氮调控模拟研究[D]. 泰安: 山东农业大学, 2019.
	WANG L. Dynamic analysis and nitrogen application simulation on N₂O emission from wheat-maize farmland in Huang-Huai-Hai region[D]. Taian: Shandong Agricultural University, 2019. (in Chinese)
[19]	李建政. 农田生态系统温室气体减排技术评价及实证分析[D]. 北京: 中国农业科学院, 2015.
	LI J Z. Technology evaluation for greenhouse gas mitigation in cropland ecosystem and case studies[D]. Beijing: Chinese Academy of Agricultural Sciences, 2015. (in Chinese)
[20]	马晨光, 蔡焕杰, 卢亚军. 基于APSIM模型不同水氮处理下N₂O的排放研究. 灌溉排水学报, 2020, 39(11): 120-129.
	MA C G, CAI H J, LU Y J. Modelling the impact of water and nitrogen application on N₂O emission from farmland using the APSIM model. Journal of Irrigation and Drainage, 2020, 39(11): 120-129. (in Chinese)
[21]	刘兴宇, 李广, 袁建钰, 徐万恒, 赵萍. 不同耕作措施下温度升高对旱作春小麦农田土壤N₂O排放的影响. 干旱地区农业研究, 2022, 40(4): 177-184.
	LIU X Y, LI G, YUAN J Y, XU W H, ZHAO P. Effects of temperature rise under different tillage measures on N₂O emission in dryland spring wheat farmland soil. Agricultural Research in the Arid Areas, 2022, 40(4): 177-184. (in Chinese)
[22]	WATKINS W A, SCHEVILL W E. Aerial observation of feeding behavior in four baleen whales: Eubalaena glacialis, Balaenoptera borealis, Megaptera novaeangliae, and Balaenoptera physalus. Journal of Mammalogy, 1979, 60(1): 155-163.
[23]	MIRJALILI S, LEWIS A. The whale optimization algorithm. Advances in Engineering Software, 2016, 95: 51-67.
[24]	牛培峰, 吴志良, 马云鹏, 史春见, 李进柏. 基于鲸鱼优化算法的汽轮机热耗率模型预测. 化工学报, 2017, 68(3): 1049-1057. doi: 10.11949/j.issn.0438-1157.20161099
	NIU P F, WU Z L, MA Y P, SHI C J, LI J B. Prediction of steam turbine heat consumption rate based on whale optimization algorithm. CIESC Journal, 2017, 68(3): 1049-1057. (in Chinese)
[25]	CHAI T, DRAXLER R R. Root mean square error (RMSE) or mean absolute error (MAE)? -Arguments against avoiding RMSE in the literature. Geoscientific Model Development, 2014, 7(3): 1247-1250.
[26]	SHCHERBAKOV M, BREBELS A, SHCHERBAKOVA N L, TYUKOV A, JANOVSKY T A, KAMAEV V A. A survey of forecast error measures. World Applied Sciences Journal, 2013, 24(24): 171-176.
[27]	陶惠林, 徐良骥, 冯海宽, 杨贵军, 杨小冬, 苗梦珂, 代阳. 基于无人机数码影像的冬小麦株高和生物量估算. 农业工程学报, 2019, 35(19): 107-116.
	TAO H L, XU L J, FENG H K, YANG G J, YANG X D, MIAO M K, DAI Y. Estimation of plant height and biomass of winter wheat based on UAV digital image. Transactions of the Chinese Society of Agricultural Engineering, 2019, 35(19): 107-116. (in Chinese)
[28]	HASTIE T, TIBSHIRANI R, FRIEDMAN J H. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2nd ed. New York: Springer, 2009.
[29]	LI J Z, WANG L G, LUO Z K, WANG E L, WANG G C, ZHOU H, LI H, XU S W. Reducing N₂O emissions while maintaining yield in a wheat-maize rotation system modelled by APSIM. Agricultural Systems, 2021, 194: 103277.
[30]	FANG Q X, MA L, HALVORSON A D, MALONE R W, AHUJA L R, DEL GROSSO S J, HATFIELD J L. Evaluating four nitrous oxide emission algorithms in response to N rate on an irrigated corn field. Environmental Modelling & Software, 2015, 72: 56-70.
[31]	LI Y, CHEN D L, ZHANG Y M, EDIS R, DING H. Comparison of three modeling approaches for simulating denitrification and nitrous oxide emissions from loam-textured arable soils. Global Biogeochemical Cycles, 2005, 19(3): GB3002.

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参数 Parameter	土壤深度 Soil depth (cm)
参数 Parameter	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

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

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

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

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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

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

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