Nitrogen oxide emissions from cropland soil: spatiotemporal heterogeneity, carbon-nitrogen coupling mechanisms, and mitigation strategies

Nitrogen oxides (N₂O, NO, and NO₂) serve as critical linkages connecting climate systems, ecosystems, and atmospheric chemistry, with soils acting as a primary natural source. Adopting a multi-scale framework spanning global, regional, and field scales, we systematically examine the spatiotemporal heterogeneity of nitrogen oxide emissions from cropland soils. Spatially, emissions exhibit a latitudinal gradient, decreasing from low to high latitudes, with hotspots concentrated in agriculturally intensive regions. Temporally, emissions display multi-scale rhythmic patterns aligned with crop growth stages, seasonal cycles, and diurnal variations, tightly coupled to soil carbon-nitrogen transformation processes. From the perspective of carbon-nitrogen coupling mechanisms, we reveal how land management practices—including nitrogen fertilization, conservation tillage, and precision irrigation—regulate emissions by modulating soil organic carbon content, carbon-nitrogen ratios, and pore structure. Concurrently, climate change drivers such as rising temperatures, elevated CO₂ concentrations, and extreme precipitation alter microbial-mediated carbon-nitrogen transformation efficiency, collectively shaping the core mechanisms governing nitrogen oxide emissions. A meta-analysis further investigates light effects on soil nitrogen oxide emissions, demonstrating significant impacts: light exposure increased N₂O and NO fluxes by 57.28% and 116.19%, respectively. Notably, heightened UV-B radiation reduced N₂O emissions by 6.85%, whereas shading increased them by 77.23%, with crop-specific responses observed. Mechanistically, light regulates emissions by modifying soil physicochemical properties and restructuring nitrogen-cycling microbial communities. Current emission mitigation faces challenges, including underdeveloped monitoring systems, limited prediction accuracy due to multifactor coupling complexities, and poor regional adaptability of existing technologies. Integrating multi-source data (field observations, remote sensing inversion, laboratory experiments) with advanced modeling approaches—such as climate-soil-crop coupling models and machine learning algorithms—offers viable pathways to enhance emission prediction precision and optimize mitigation strategies. Looking ahead, priorities include establishing multi-scale automated monitoring networks, developing carbon-nitrogen coupling-driven predictive models, promoting regionally tailored carbon sequestration and nitrogen emission reduction technologies, and combining policy incentives with public engagement to reduce uncertainties in global carbon-nitrogen cycle projections. These efforts aim to strengthen scientific support for sustainable agricultural development.