The agro-pastoral ecotone of Gansu Province, a critical component of the ecological security barrier in northern China, is characterized by pronounced ecological fragility and climatic sensitivity. Investigating vegetation dynamics in this region is essential for balancing ecological conservation and sustainable development. This study integrated MODIS/NDVI remote sensing data (2000–2020), climate, land, and anthropogenic factors, employing Sen’s slope analysis, coefficient of variation (Cv), Hurst index, geodetector modeling, and partial correlation analysis to systematically unravel the spatio-temporal evolution and driving mechanisms of vegetation coverage. Key findings revealed the following: (1) Vegetation coverage exhibited a significant increasing trend (0.05 decade⁻¹), peaking in 2018 (NDVI = 0.71), with a distinct north–south spatial gradient (lower values in northern areas vs. higher values in southern regions). Statistically significant greening trends (p < 0.05) were observed in 55.42% of the study area. (2) Interannual vegetation fluctuations were generally mild (Cv = 0.15), yet central regions showed 2–3 times higher variability than southern/northwestern areas. Future projections (H = 0.62) indicated sustained NDVI growth. (3) Climatic factors dominated vegetation dynamics, with sunshine hours and precipitation exhibiting the strongest explanatory power (q = 0.727 and 0.697, respectively), while the elevation–precipitation interaction achieved peak explanatory capacity (q = 0.845). (4) NDVI correlated positively with precipitation in 43.62% of the region (r_mean = 0.47), whereas average temperature, maximum temperature, ≥10 °C accumulated temperature, and sunshine hours suppressed vegetation growth (r_mean = −0.06 to −0.42), confirming precipitation as the primary driver of regional vegetation recovery. The multi-scale analytical framework developed here provides methodological and empirical support for precision ecological governance in climate-sensitive transitional zones, particularly for optimizing ecological barrier functions in arid and semi-arid regions.

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.

Water and nitrogen availability jointly influence dryland wheat yield and grain protein formation; the ability of crop models to simulate the synergistic effects of these two factors depends on the accuracy of key parameters. However, the large number of model parameters and their complex interactions make parameter screening and calibration challenging. In this study, the model was calibrated using annual yield records for dryland wheat in Anding District, Dingxi City, and Gansu Province, from 2015 to 2019, along with field observation data (grain protein content) from Anjiagou Village, Fengxiang Town, and Anding District, collected during the same period. The model was then validated using independent data from two growing seasons (2020–2021) in the same study area. Based on the Agricultural Production sIMulator Next-Generation (APSIM NG) and using water–nitrogen response as the foundation, a multi-objective parameter calibration framework was developed with yield and grain protein content as calibration targets. The extended Fourier amplitude test (EFAST) was used to screen key model parameters, and the performance of the DREAM–zs algorithm, the Nelder–Mead simplex algorithm, and the multi-objective rime optimization algorithm (MORIME) in calibrating these parameters was compared and analyzed. All three algorithms reduced the simulation error in yield; for grain protein content, the Nelder–Mead and MORIME algorithms reduced the simulation error, while DREAM-zs did not. The simulation accuracy of the Nelder–Mead and MORIME algorithms was generally similar, but MORIME yielded a set of Pareto solutions that characterized the trade-off between the simulation accuracy of yield and grain protein content; therefore, its parameter solutions were selected for independent validation. The validation results showed that the RMSE for yield decreased from 248.45 kg hm⁻² to 213.90 kg hm⁻², and the NRMSE decreased from 12.60% to 10.85%; for grain protein content, the RMSE decreased from 0.280% to 0.250%, and the NRMSE decreased from 1.86% to 1.66%. Furthermore, the incorporation of parallel computing reduced MORIME’s runtime by 82.99%. The study demonstrates that MORIME with parallel computing can provide methodological support for the multi-objective, efficient optimization of crop model parameters.