Scientia Agricultura Sinica ›› 2025, Vol. 58 ›› Issue (13): 2538-2551.doi: 10.3864/j.issn.0578-1752.2025.13.004

• TILLAGE & CULTIVATION·PHYSIOLOGY & BIOCHEMISTRY·AGRICULTURE INFORMATION TECHNOLOGY • Previous Articles     Next Articles

Multi-Objective Optimization of Stable Yield and Emission Reduction of Dryland Spring Wheat Based on DNDC and NSGA-Ⅲ. Coupling Model

CAO JingWen1(), NIE ZhiGang1,2(), LI Guang2,3, YANG Jie4   

  1. 1 School of Information Science and Technology, Gansu Agricultural University, Lanzhou 730070
    2 Gansu Agricultural University State Key Laboratory of Aridland Crop Science, Lanzhou 730070
    3 HeXi University, Zhangye 734000, Gansu
    4 College of Pratacultural Science of Gansu Agricultural University, Lanzhou 730070
  • Received:2024-12-31 Accepted:2025-05-30 Online:2025-07-01 Published:2025-07-05

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

Table 1

Input parameters of DNDC model"

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 (℃)

Fig. 1

Precipitation in the study area in 1970-2023"

Fig. 2

Simulation and verification of spring wheat yield from 2021 to 2023 W0、W150分别表示灌溉量0和150 mm;CK、LN、MN、HN分别表示不施氮、低氮(55 kg·hm-2)、中氮(110 kg·hm-2)和高氮(220 kg·hm-2)处理 In the figure, W0 and W150 represent irrigation of 0 and 150 mm, respectively; CK, LN, MN, and HN represent no nitrogen application, low nitrogen application (55 kg·hm-2), medium nitrogen application (110 kg·hm-2), and high nitrogen application (220 kg·hm-2), respectively。下同 The same as below"

Fig. 3

Verification of soil CO2 emission flux simulation from 2021 to 2023"

Fig. 4

Verification of soil N2O emission flux simulation from 2021 to 2023"

Fig. 5

The pareto non-dominated solution chart"

Fig. 6

The Pareto non-dominated solution chart"

Fig. 7

Spring wheat yield distribution under different irrigation and fertilization conditions"

Fig. 8

Distribution of soil CO2 emission fluxes from spring wheat under different irrigation and fertilization conditions"

Fig. 9

Distribution of soil N2O emission fluxes from spring wheat under different irrigation and fertilization conditions"

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