基于DNDC和NSGA-Ⅲ耦合模型的旱地春小麦稳产减排多目标优化

doi:10.3864/j.issn.0578-1752.2025.13.004

摘要/Abstract

摘要：

【目的】为应对日益增长的粮食需求和生态可持续性要求，探讨灌溉与施肥综合调控对西北地区旱地春小麦产量、土壤CO₂及N₂O排放通量的综合影响，以明确最优的灌溉量及施肥策略，实现农业生产与环境效益的协调发展。【方法】基于DNDC农业生态系统模拟模型，立足于2021—2023年甘肃省定西市安定区凤翔镇安家坡村的田间试验数据，对模型进行参数校准和验证，构建不同施肥水平（0—400 kg·hm^-2）与灌溉水平（0—300 mm）的管理情景，探索不同灌溉量与施肥量管理措施下的小麦生长动态和土壤温室气体（CO₂、N₂O）排放通量的响应规律，结合NSGA-Ⅲ多目标优化算法构建多目标优化框架，以“最大化作物产量”“最小化土壤CO₂排放通量”“最小化土壤N₂O排放通量”为三目标函数，实现小麦产量提升与土壤温室气体减排的协同优化，确定可以兼顾产量和环境效益的最优管理方案。【结果】 DNDC模型能够较好地模拟春小麦产量及土壤温室气体排放通量。在4种施肥梯度处理下，3年间产量、土壤CO₂和N₂O排放通量的归一化均方根误差（NRMSE）分别为17.4%—18.8%、7.62%—11.41%和9.19%—12.47%；在2种灌溉量处理下，3年间产量的归一化均方根误差（NRMSE）为13.3%—17.2%。优化后的灌溉量与施肥量表明，当施肥量控制在150—180 kg·hm^-2、灌溉量110—150 mm时，小麦产量可提升至2 088.48 kg·hm^-2，同时土壤CO₂排放通量控制在每年4 998.87—5 011.50 kg·hm^-2，土壤N₂O排放通量控制在每年4.06—4.14 kg·hm^-2。【结论】耦合DNDC模型与NSGA-Ⅲ算法可实现旱地春小麦产量与土壤温室气体排放通量的协同优化，当灌溉量为110—150 mm、施氮量为150—180 kg·hm^-2时，可在保障产量稳定的同时有效控制土壤CO₂及N₂O排放通量，为陇中旱地春小麦稳产减排提供科学依据。

关键词: 春小麦, 产量, DNDC模型, NSGA-Ⅲ, 温室气体排放, 多目标优化算法

Abstract:

【Objective】 In response to growing food demand and ecological sustainability requirements, this study explored the comprehensive impact of integrated irrigation and fertilization management on spring wheat yield, soil CO₂ and N₂O emissions fluxes in arid areas of Northwest China, with the aim of identifying optimal irrigation and fertilization strategies to achieve coordinated development of agricultural production and environmental benefits. 【Method】 Based on the DNDC agricultural ecosystem simulation model, using field trial data from Anjiapo Village, Fengxiang Town, Dingxi City, Gansu Province, from 2021 to 2023, the model was calibrated and validated. Different fertilization levels (0-400 kg·hm^-2) and irrigation levels (0-300 mm). The model simulates the response patterns of wheat growth dynamics and soil greenhouse gas (CO₂ and N₂O) emission fluxes under different irrigation and fertilization management measures. Combining the NSGA-III multi-objective optimization algorithm, a multi-objective optimization framework was established with three objective functions: “maximizing crop yield”, “minimizing soil CO₂ emissions flux”, and “minimizing soil N₂O emissions flux” as the three objective functions, achieving synergistic optimization between wheat yield enhancement and soil greenhouse gas emission reduction, and determining the optimal management scheme that balances yield and environmental benefits. 【Result】 The DNDC model effectively simulates spring wheat yield and soil greenhouse gas emission fluxes. Under four fertilization gradient treatments, the normalized root mean square error (NRMSE) for yield, soil CO₂ emissions, and N₂O emissions over three years was 17.4%-18.8%, 7.62%-11.41%, and 9.19%-12.47%, respectively. Under two irrigation treatments, the normalized root mean square error NRMSE for yield over three years was 13.3%-17.2%. The optimized irrigation and fertilization rates indicate that when fertilization is controlled at 150-180 kg·hm^-2 and irrigation volume is 110-150 mm, wheat yield can be increased to 2 088.48 kg·hm^-2, while soil CO₂ emissions flux is controlled at 4 998.87-5 011.5 kg·hm^-2 per year, and soil N₂O emission flux is controlled at 4.06-4.14 kg·hm^-2 per year. 【Conclusion】 Coupling the DNDC model with the NSGA-III algorithm enables the simultaneous optimization of spring wheat yield and soil greenhouse gas emissions fluxes in dryland areas. When the irrigation amount is set between 110-150 mm and nitrogen application rate between 150-180 kg·hm^-2, it is possible to maintain stable yields while effectively controlling soil CO₂ and N₂O emission fluxes. This provides a scientific basis for achieving both yield stability and emission reduction in dryland spring wheat systems in central Gansu.

Key words: spring wheat, yield, DNDC model, NSGA-Ⅲ, greenhouse gas emissions, multi-objective optimization algorithm

曹景文, 聂志刚, 李广, 杨洁. 基于DNDC和NSGA-Ⅲ耦合模型的旱地春小麦稳产减排多目标优化[J]. 中国农业科学, 2025, 58(13): 2538-2551.

CAO JingWen, NIE ZhiGang, LI Guang, YANG Jie. Multi-Objective Optimization of Stable Yield and Emission Reduction of Dryland Spring Wheat Based on DNDC and NSGA-Ⅲ. Coupling Model[J]. Scientia Agricultura Sinica, 2025, 58(13): 2538-2551.

0
/ / 推荐

导出引用管理器 EndNote|Reference Manager|ProCite|BibTeX|RefWorks

链接本文: https://www.chinaagrisci.com/CN/10.3864/j.issn.0578-1752.2025.13.004

https://www.chinaagrisci.com/CN/Y2025/V58/I13/2538

图/表 10

表1

图1

图2

图3

图4

图5

图6

图7

图8

图9

参考文献 35

[1]	曾晨, 周詹杭, 柯新利, 刘文平, 王真. 全球气候变化下农业可持续发展的研究现状和学科交叉分析. 华中农业大学学报, 2024, 43 (6): 6-16.
	ZENG C, ZHOU Z, KE X, LIU W, WANG Z. Review and interdisciplinary analysis of sustainable development of agriculture in context of global climate change. Journal of Huazhong Agricultural University, 2024, 43 (6): 6-16. (in Chinese)
[2]	KROPP I, NEJADHASHEMI A P, DEB K, ABOUALI M, ROY P C, ADHIKARI U, HOOGENBOOM G. A multi-objective approach to water and nutrient efficiency for sustainable agricultural intensification. Agricultural Systems, 2019, 173: 289-302.
[3]	ZHOU C Q, ZHAO W J, LI H L, MA F, WU K Q. Simulation and evaluation of tomato growth by AquaCrop model under different agricultural waste materials. International Journal of Agricultural and Biological Engineering, 2024, 17(5): 112-119.
[4]	LÜ J Y, JIANG Y N, XU C, LIU Y J, SU Z H, LIU J C, HE J Q. Multi-objective winter wheat irrigation strategies optimization based on coupling AquaCrop-OSPy and NSGA-III: A case study in Yangling, China. Science of The Total Environment, 2022, 843: 157104.
[5]	GROOT J C J, OOMEN G J M, ROSSING W A H. Multi-objective optimization and design of farming systems. Agricultural Systems, 2012, 110: 63-77.
[6]	程杜. 基于代理辅助多目标优化的农业决策系统[D]. 长春: 长春师范大学, 2023.
	CHENG D. Agricultural decision-making system based on agent- assisted multi-objective optimization[D]. Changchun: Changchun Normal University, 2023. (in Chinese)
[7]	YAN Y L, RYU Y, LI B L, DECHANT B, ZAHEER S A, KANG M. A multi-objective optimization approach to simultaneously halve water consumption, CH₄, and N₂O emissions while maintaining rice yield. Agricultural and Forest Meteorology, 2024, 344: 109785.
[8]	XIAO L, WANG G, WANG E, LIU S, CHANG J, ZHANG P, ZHOU H, WEI Y, ZHANG H, ZHU Y. Spatiotemporal co-optimization of agricultural management practices towards climate-smart crop production. Nature Food, 2024, 5(1): 59-71. doi: 10.1038/s43016-023-00891-x pmid: 38168779
[9]	王凯航. 基于AquaCrop-FloPy和NSGA-Ⅲ耦合模型的TK601玉米新品种灌溉制度多目标优化[D]. 杨凌: 西北农林科技大学, 2023.
	WANG K H. Multi-objective optimization of irrigation system for new TK601 maize variety based on AquaCrop-FloPy and NSGA-III coupled model[D]. Yangling: Northwest Agriculture and Forestry University, 2023. (in Chinese)
[10]	王永强. 基于作物模型与遥感数据同化的区域尺度夏玉米生长模拟与灌溉施肥制度优化[D]. 杨凌: 西北农林科技大学, 2023.
	WANG Y Q. Regional-scale summer corn growth simulation and irrigation and fertilization regime optimization based on crop models and remote sensing data assimilation[D]. Yangling: Northwest A & F University, 2023. (in Chinese)
[11]	杜梦寅, 袁建钰, 李广, 闫丽娟, 刘兴宇, 祁小平, 庞晔. 保护性耕作对黄土高原半干旱区农田土壤N₂O排放的影响. 干旱区研究, 2022, 39(2): 493-501. doi: 10.13866/j.azr.2022.02.17
	DU M Y, YUAN J Y, LI G, YAN L J, LIU X Y, QI X P, PANG Y. Effects of conservation tillage on N₂O emissions from agricultural soils in the semi-arid zone of the Loess Plateau. Arid Zone Research, 2022, 39(2): 493-501. (in Chinese)
[12]	成思潮, 李广, 姚瑶, 袁建钰, 何锦煜. DNDC模型模拟不同施肥水平下旱作麦田土壤N₂O排放及其敏感性分析. 甘肃农业大学学报, 2024, 59(4): 44-54.
	CHENG S C, LI G, YAO Y, YUAN J Y, HE J Y. Simulation and sensitivity analysis of N₂O emissions in dryland wheat fields at different fertilization levels using the DNDC model. Journal of Gansu Agricultural University, 2024, 59(4): 44-54. (in Chinese)
[13]	吕凤莲, 杨学云, 赵冉, 单晓玲, 王九军, 郑伟, 张树兰. 静态箱/气相色谱法监测农田温室气体排放研究. 实验技术与管理, 2022, 39(9): 15-24.
	LÜ F L, YANG X Y, ZHAO R, SHAN X L, WANG J J, ZHENG W, ZHANG S L. Research on monitoring greenhouse gas emissions from farmland by static chamber/gas chromatography. Experimental Technology and Management, 2022, 39(9): 15-24. (in Chinese)
[14]	黄少辉. 小麦-玉米轮作体系生态集约化管理下碳氮循环特征研究[D]. 北京: 中国农业科学院, 2021.
	HUANG S H. Characterization of carbon and nitrogen cycling under ecological intensive management of wheat-maize crop rotation system[D]. Beijing: Chinese Academy of Agricultural Sciences, 2021. (in Chinese)
[15]	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)
[16]	肖玉涛, 李正鹏, 宋明丹, 韩梅. 基于DNDC模型青海西宁地区春小麦施氮量研究. 中国土壤与肥料, 2024, (3): 87-95.
	XIAO Y T, LI Z P, SONG M D, HAN M. Study on nitrogen application to spring wheat in Xining area of Qinghai based on DNDC model. China Soil and Fertilizer, 2024, (3): 87-95. (in Chinese)
[17]	郝琪, 陈天陆, 王富贵, 王振, 白岚方, 王永强, 王志刚. 基于无人机多光谱数据和氮素空间分异的玉米冠层氮浓度估算. 作物学报, 2025, 51(1): 189-206. doi: 10.3724/SP.J.1006.2025.43015
	HAO Q, CHEN T L, WANG F G, WANG Z, BAI L F, WANG Y Q, WANG Z G. Estimation of canopy nitrogen concentration in maize based on UAV multi-spectral data and spatial nitrogen heterogeneity. Acta Agronomica Sinica, 2025, 51(1): 189-206. (in Chinese) doi: 10.3724/SP.J.1006.2025.43015
[18]	王钧, 李广, 聂志刚, 董莉霞, 闫丽娟. 陇中黄土高原区旱地春小麦产量对干旱胁迫响应的模拟研究. 干旱区地理, 2021, 44(2): 494-506. doi: 10.12118/j.issn.1000–6060.2021.02.20
	WANG J, LI G, NIE Z G, DONG L X, YAN L J. Simulation study on yield response of dryland spring wheat to drought stress in Longzhong Loess Plateau area. Arid Zone Geography, 2021, 44(2): 494-506. (in Chinese)
[19]	聂志刚, 任新庄, 李广, 雒翠萍, 董莉霞, 王钧, 逯玉兰. 旱地小麦产量及其构成因素灌溉效应的模拟分析. 干旱地区农业研究, 2019, 37(3): 117-122.
	NIE Z G, REN X Z, LI G, LUO C P, DONG L X, WANG J, LU Y L. Simulation analysis of irrigation effect on yield andits constitute factors of dryland wheat. Agricultural Research in the Arid Areas, 2019, 37(3): 117-122. (in Chinese)
[20]	李文桦, 明梦君, 张涛, 王锐, 黄生俊, 王凌. 考虑全局和局部帕累托前沿的多模态多目标优化算法. 自动化学报, 2023, 49(1): 148-160.
	LI W H, MING M J, ZHANG T, WANG R, HUANG S J, WANG L. A multimodal multi-objective optimization algorithm considering global and local Pareto frontiers. Journal of Automation, 2023, 49(1): 148-160. (in Chinese)
[21]	JIANG W, ZHU A, WANG C, ZHANG F S, JIAO X Q. Optimizing wheat production and reducing environmental impacts through scientist-farmer engagement: Lessons from the North China Plain. Food and Energy Security, 2021, 10(1): e255. doi: 10.1002/fes3.255 pmid: 33791100
[22]	LÜ X D, WANG T, MA Z M, ZHAO C Y, SIDDIQUE K H M, JU X T. Enhanced efficiency nitrogen fertilizers maintain yields and mitigate global warming potential in an intensified spring wheat system. Field Crops Research, 2019, 244: 107624.
[23]	LINKER R, SYLAIOS G. Efficient model-based sub-optimal irrigation scheduling using imperfect weather forecasts. Computers and Electronics in Agriculture, 2016, 130: 118-127.
[24]	HUANG X M, XU X P, ZHU Q C, ZHANG Y T. Optimizing water and nitrogen inputs for sustainable wheat yields and minimal environmental impacts. Agricultural Systems, 2024, 220: 104061.
[25]	SONG X T, LIU M, JU X T, GAO B, SU F, CHEN X P, REES R M. Nitrous oxide emissions increase exponentially when optimum nitrogen fertilizer rates are exceeded in the North China Plain. Environmental Science & Technology, 2018, 52(21): 12504-12513.
[26]	YANG Y, TONG Y A, GAO P C, HTUN Y M, FENG T. Evaluation of N₂O emission from rainfed wheat field in northwest agricultural land in China. Environmental Science and Pollution Research, 2020, 27(35): 43466-43479.
[27]	PRINA M G, COZZINI M, GAREGNANI G, MANZOLINI G, MOSER D, FILIPPI OBEREGGER U, PERNETTI R, VACCARO R, SPARBER W. Multi-objective optimization algorithm coupled to EnergyPLAN software: The EPLANopt model. Energy, 2018, 149: 213-221.
[28]	郭乙霏, 张利平, 王纲胜, 尚瑞朝, 张欢, 祝志勇, 王长仲. 耕作方式与水肥组合对小麦-玉米田温室气体排放的影响. 农业工程学报, 2022, 38(13): 95-104.
	GUO Y F, ZHANG L P, WANG G S, SHANG R C, ZHANG H, ZHU Z Y, WANG C Z. Effects of the tillage and combination of water and fertilizer on the greenhouse gas emissions of wheat-maize field. Transactions of the Chinese Society of Agricultural Engineering, 2022, 38(13): 95-104. (in Chinese)
[29]	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. Study on N₂O emission under different water nitrogen treatments based on APSIM model. Journal of Irrigation and Drainage, 2020, 39(11): 120-129. (in Chinese)
[30]	ZHANG J, HU K L, LI K J, ZHENG C L, LI B G. Simulating the effects of long-term discontinuous and continuous fertilization with straw return on crop yields and soil organic carbon dynamics using the DNDC model. Soil and Tillage Research, 2017, 165: 302-314.
[31]	张丽霞, 杨永辉, 尹钧, 武继承, 潘晓莹. 水肥一体化对小麦干物质和氮素积累转运及产量的影响. 农业机械学报, 2021, 52(2): 275-282, 319.
	ZHANG L X, YANG Y H, YIN J, WU J C, PAN X Y. Effects of drip fertigation on accumulation and translocation of dry matter and nitrogen together with yield in wheat. Transactions of the Chinese Society for Agricultural Machinery, 2021, 52(2): 275-282, 319. (in Chinese)
[32]	ZHAO K Q, ZHAO X C, HE L Q, WANG N Y, BAI M, ZHANG X B, CHEN G, CHEN A W, LUO L, ZHANG J C. Comprehensive assessment of straw returning with organic fertilizer on paddy ecosystems: A study based on greenhouse gas emissions, C/N sequestration, and risk health. Environmental Research, 2025, 266: 120519.
[33]	李越, 李根东, 陈志君, 张雪晨, 黄冠华. 基于氮收支平衡的河套灌区春小麦农田灌溉和施氮策略. 农业工程学报, 2022, 38(17): 61-72.
	LI Y, LI G D, CHEN Z J, ZHANG X C, HUANG G H. Irrigation and nitrogen application strategies for spring wheat farmland in Hetao Irrigation District based on nitrogen balance. Journal of Agricultural Engineering, 2022, 38(17): 61-72. (in Chinese)
[34]	ZHANG X, XIAO G M, BOL R, WANG L G, ZHUGE Y P, WU W L, LI H, MENG F Q. Influences of irrigation and fertilization on soil N cycle and losses from wheat-maize cropping system in northern China. Environmental Pollution, 2021, 278: 116852.
[35]	李长生. 生物地球化学:科学基础与模型方法. 北京: 清华大学出版社, 2016.
	LI C S. Biogeochemistry:Scientific Basis and Modeling Methods. Beijing: Tsinghua University Press, 2016. (in Chinese)

参数种类 Parameter type	参数名称 Parameter name	取值 Value
气候参数 Climate parameter	纬度 Latitude	35.58
	降水中的N浓度 N concentration in rainfall	1 (mg N·ppm^-1)
	空气中CO₂浓度 Carbon dioxide concentration in the air	400 (ppm)
土壤参数 Soil parameter	土壤质地 Texture	粉质壤土 Silt loam
	容重 Soil bulk density	1.27 (g·cm^-3)
	田间持水量 Field capacity	0.35 (%)
	萎蔫点 Wilting point	0.25 (%)
	黏粒含量 Clay content	0.14 (%)
	孔隙度 Porosity	0.485 (%)
	pH	8.36
作物参数 Crop parameter	生物量比例 Biomass fraction	0.41/0.11/0.29/0.19
	生物量碳氮比 Biomass carbon to nitrogen ratio	40/90/90/65
	对氮素需求量 Demand for nitrogen	129.45 (kg·hm^-2)
	生长积温 Accumulated temperature of growth	2700 (℃)
	最适温度 Optimum temperature	22 (℃)