Process-based crop growth models, as an important analytical tool in agricultural production, face the problem of calibrating many parameters during the application process, and sensitivity analysis (SA) can quantify the effects of the model input parameters on the model output and provide an important basis for parameter calibration. In this study, we combined the good performance of the Agricultural Production Systems sIMulator Next-Generation (APSIM NG) model in simulating crop growth, soil carbon and nitrogen cycles, and soil N₂O emissions with the efficient computational efficiency of the extended Fourier amplitude test (EFAST) method. The sensitivity of the APSIM NG model to the simulation of soil N₂O emissions was systematically investigated using the EFAST method in a dry-crop wheat field in the semi-arid region of the Loess Plateau in Longzhong, China, where 28 crop cultivar parameters, 15 soil parameters, 4 meteorological parameters, and 4 field management parameters were selected. The parameters were selected based on the existing literature and the official documents of the model, and the parameter boundaries were determined based on the initial values of the APSIM NG model and the measured data and adjusted upward and downward by the standard normal distribution. In this study, parameters with a first-order sensitivity index ($S_i$) > 0.05 and a total sensitivity index ($S_{Ti}$) > 0.10 were identified as having a significant influence on the model outputs. The results of this study demonstrated that soil N₂O emission modeling in dry-crop wheat fields showed high sensitivity to the following parameters: (1) Among the crop cultivar parameters, the sensitivity from high to low was the leaf appearance rate, maximum leaf area, maximum nitrogen concentration of the grain, and thermal time from the starting grain-fill stage to end grain-fill stage. (2) Among the soil parameters, the sensitivity from high to low was a lower effective moisture limit, wilting coefficient, and ammonium nitrogen content. (3) Among the meteorological parameters, precipitation and solar radiation showed high sensitivity. (4) Among the field management parameters, the nitrogen application rate exhibited the most significant sensitivity. For this reason, we believe that by prioritizing the calibration of the most sensitive parameters through the results of the sensitivity analysis in this study, the workload of the APSIM NG model in the calibration process can be effectively reduced, which is conducive to the rapid localization and application of the model.

Nitrous oxide (N₂O), as one of the important greenhouse gases in the atmosphere, has a significant impact on global climate change. Its emissions are significantly regulated by land use changes, especially in ecologically fragile semi-arid areas. However, there is still a lack of systematic analysis on the key biotic and abiotic factors through which different land use patterns affect N₂O emissions. Therefore, this study focuses on four typical land use types in the Loess Plateau of central Gansu: Picea asperata (PA), Medicago sativa (MS), Abandoned land (AL), and Wheat field (WF). Static box gas chromatography was used to monitor soil N₂O flux in situ, and multidimensional analysis was conducted based on soil physicochemical properties, microbial community structure, and nitrogen cycling functional genes. Based on the observational data from the 2024 growing season (April to October), Research findings show that the cumulative emissions of N₂O from wheat fields increased significantly by 26.4%, 19.4%, and 39.8% compared to medicago sativa, abandoned land, and picea asperata, respectively. Mechanism analysis reveals that picea asperata promote nitrogen fixation and absorption in soil through higher soil water content and organic carbon content, as well as enrichment of Proteobacteria and high expression of nrfA and napA genes, thereby inhibiting N₂O production and emissions. The wheat fields, on the other hand, have significantly increased N₂O emissions due to the increased abundance of amoA_B, nxrB, and nirK functional genes and enhanced urease activity, which promote nitrification and denitrification processes. The Partial Least Squares Path Model (PLS-PM) further confirmed that nitrification functional genes are key driving factors for N₂O emissions. This study systematically reveals the microbial and biochemical pathways involved in regulating N₂O emissions through land use in semi-arid regions, providing a theoretical basis for regional nitrogen cycle management and climate mitigation.