It is of great significance to establish maize water and fertilizer application schemes under multi-objective conditions to improve water- and fertilizer-use efficiency, reduce agricultural greenhouse gas emissions, and promote sustainable agricultural developments. This study aims to analyze the effects of different water and fertilizer combinations on the summer maize yield, water-use efficiency, and field N₂O flux and to determine the optimal water and fertilizer application scheme for summer maize. Field experiments were conducted in 2023, with a total of 15 different combinations of upper and lower limits of irrigation and fertilizer levels. A binary quadratic regression model based on the yield, water-use efficiency, and N₂O emission flux was constructed. The fast non-dominated sorting genetic algorithm III (NSGA-III) was employed for verification and solution finding to simulate the optimal water and fertilizer regime. The results indicate that with increasing water and fertilizer applications, the field N₂O emission flux gradually increases. The summer maize yield and water-use efficiency show a trend of initially increasing and then decreasing. Compared to fertilization, irrigation has a more significant impact on the summer maize yield and water-use efficiency, while fertilization notably influences the field N₂O emission flux to a greater extent. Using NSGA-III, the simulated optimal water and fertilizer combination showed no significant difference in the yield and water-use efficiency compared to the actual optimal water–fertilizer irrigation combination (moderate water and moderate fertilizer), with a 3.12% increase in the field N₂O emission flux, a 15.30% decrease in the irrigation amount, and an 11.90% reduction in the fertilizer application. In conclusion, employing the optimized water and fertilizer combination can reduce agricultural irrigation and fertilization while ensuring crop yields, providing theoretical support for the green, efficient, and sustainable development of the summer maize industry.

Identifying a water–nitrogen coupling strategy to achieve high efficiency, emission reduction, and optimal yield in summer maize under multi-objective conditions is crucial for enhancing nitrogen fertilizer utilization and promoting agricultural sustainability. This study conducted a field experiment on water–fertilizer coupling in summer maize, with three irrigation levels (60%θ_f, 70%θ_f, 80%θ_f, with θ_f representing field capacity) and four nitrogen application levels (0, 180, 270, 360 kg/ha). It analyzed variations in yield, partial factor productivity of nitrogen fertilizer (PFP_N), and the soil CO₂ emission flux across different water–nitrogen combinations, establishing a multi-vector optimization model. NSGA-III (non-dominated Sorting Genetic Algorithm III) was utilized to determine the most effective combination of water and nitrogen. The results indicated that maize yield initially increases and then declines as irrigation and nitrogen levels rise. PFP_N showed a decreasing trend, and its decline gradually decreased with increasing irrigation levels, suggesting that water can alleviate nitrogen stress to some extent. Soil carbon dioxide exhalation intensity increased with both irrigation and nitrogen levels. The NSGA-III optimization revealed that the optimal water–nitrogen ratio is 1086.28 m³/ha for irrigation and 265.79 kg/ha for nitrogen. Compared with the best water–nitrogen combination (W2N3) from the experiment, this optimized scheme showed no significant difference in irrigation volume, yield, or soil CO₂ emission flux while increasing PFP_N by 13.46% and saving 1.56% of nitrogen fertilizer. In summary, the optimized water–fertilizer coupling scheme provides a scientific basis for high-efficiency, high-yield, and low-emission maize production in Henan Province, supporting sustainable agricultural development.

Determining the influencing factors of winter wheat field carbon emissions and their dynamic trends is of great significance to study the carbon emission mechanism of winter wheat, reduce greenhouse gas emissions from agricultural fields, and promote the sustainable development of agriculture. The aim of this study is to analyze the relationship between different influencing factors and CO₂ emission fluxes in winter wheat fields and to construct a dynamic simulation model of field carbon emission so as to provide a basis for accurate and convenient calculation of CO₂ emission from wheat fields in the Henan region. This study comprehensively considered the effects of the dynamic changes in meteorological, soil, hydrological, and other factors over time on the field carbon emission during the growth process of the crop and carried out a dynamic simulation study of the field carbon emission in the experimental field with six sets of experiments, using the multiple regression method. Six groups of experiments were set up, and a multi-parameter field carbon emission dynamic model was constructed by the multiple regression method to simulate the optimal calculation model. The results showed that the simulated values of field CO₂ emissions were consistent with the trend of the measured values, and the total cumulative CO₂ emissions in fields A1, A2, and A3 were 8624.2 kg/hm², 7924.3 kg/hm², and 7531.4 kg/hm², respectively, while the model-simulated values were 9399.2 kg/hm², 8935.2 kg/hm², and 8371.1 kg/hm². The errors between the simulated and actual emissions were 7.9%, 12.8%, and 11.1%, respectively, indicating a high accuracy in the simulation results. The model developed in this study comprehensively accounts for the dynamic impacts of meteorological, soil, and hydraulic factors on CO₂ emissions, effectively reflecting the dynamic changes in field carbon emissions and achieving high calculation accuracy.