Multivariate Regression-Based Dynamic Simulation Modeling of Cumulative Carbon Emissions from Fields

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.